#include<bits/stdc++.h>
using namespace std;
typedef long long int lli;
#define inf  1e9+7
#define lp(n) for(lli i = 0; i < n; i++)
// CPP implementation of
// above algorithm


int main()
{
	lli t;
	cin>>t;
	while(t--)
	{
		lli w,h,n;
		cin>>w>>h>>n;
		int c=1;
		while((w*h)%2==0)
		{
			if(w%2==0)
				w=w/2 , c*=2;
			else if(h%2==0)
				h/=2 , c*=2;

		}
		if(c>=n)
			cout<<"YES"<<'\n';
		else
			cout<<"NO"<<'\n';
	}
}











































/*
aggressive cows
lli a[1000001];
bool isvalid(int n,int c,int mid)
{
    int cnt=0;
    int pre=-1000000009;
    for(int i=0;i<n;i++)
    {
        if(a[i]-pre>=mid)
            cnt++ , pre=a[i];
    }
    if(cnt>=c)
        return true;
    return false;

}
int getans(int n,int c)
{
    int ans=0;
    int start=0;
    int end=a[n-1]-a[0];
    while(start<=end)
    {
        int mid=(start+end)/2;
        if(isvalid(n,c,mid))
        {
            ans=max(ans,mid);
            start=mid+1;
        }
        else
            end=mid-1;
    }
    return ans;
}
int main()
{
    int t;
    cin>>t;
    while(t--)
    {
        int n,c;
        cin>>n>>c;
        //vector<int> a(n);
        for(int i=0;i<n;i++)
            cin>>a[i];
        sort(a, a+n);
        cout<<getans(n,c)<<endl;

    }
}

*/

/*
#LINEAR REGRESSION

from sklearn import datasets
from sklearn import  linear_model
from sklearn.model_selection import  train_test_split
from matplotlib import pyplot as plt


boston = datasets.load_boston()
x=boston.data
y=boston.target
print(x)
print(x.shape)
print(y)


#algo
l_reg = linear_model.LinearRegression()
#x.T = transpose of x
plt.scatter(x.T[5], y)
plt.show()

xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2)
#train
model=l_reg.fit(xtrain,ytrain)
prediction = model.predict(xtest)
print(prediction)
print(l_reg.score(x,y))
print(l_reg.coef)
print(l_reg.intercept_)
kmeans

from sklearn.datasets import load_breast_cancer
from sklearn.cluster import KMeans
from sklearn.model_selection import  train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import scale
import pandas as pd

bc = load_breast_cancer()
print(bc)

x=scale(bc.data)
print(x)

y = bc.target

xtrain, xtest, ytrain, ytest =train_test_split(x,y, test_size=0.2)
model = KMeans(n_clusters=2, random_state=0)
model.fit(xtrain)
prediction = model.predict(xtest)
labels = model.labels_

print('labels:', labels)
print('predictions:', prediction)
print('accuracy:', accuracy_score(ytest, prediction))
print('actual values:', ytest)

print(pd.crosstab(ytrain, labels))

from PIL import Image
'''
*/