Unable to overfit using MLP
Some things that could be happening: 


You do not have enough parameters in your hidden Layer to learn. Try something like [67, 512, 5] or expand deeper with something like [67, 1024, 256, 5]. The idea is that your hidden layer may be too small to capture interactions between the attributes given the amount of training data you have.


A rule of thumb that I have seen is:

Nh=Ns/(α∗(Ni+No))

Nh = number of hidden neurons.
Ni = number of input neurons.
No = number of output neurons.
Ns = number of samples in training data set.
α = an arbitrary scaling factor usually 2-10.


Which in your case gets you to like 5000 neurons, but since you are oversampling your classes, I would also suggest under-sampling instead, and that gets you maybe to around 1024, which is fairly standard. The [67, 512, 5] configuration is pure gut reaction. 


Learning rate is too high. Try starting with lower LR (like 10e-5). Sometimes this works with Adam.
Weight Initialization : double check you are using Xavier.