Confusion matrix - classes get confused with each other
The confusion matrix as the name suggest helps to identify how many of the predicted classes are being confused with true classes. For instance, in this case, the rows indicate the predicted classes and the columns indicates the correct classes. 

So if we consider the diagonal values, the predicted and the true class is the same. Hence, the diagonal values do not indicate any confusion. When we check the upper triangular part of this matrix,  the value 11 in your example indicates that 11 examples have been predicted as A when the correct values are actually B. Also, looking at (2,1)(the first value of second row) the value is 10. This implies that 10 values predicted as B actually are A. This shows that A and B are confused.

When two classes are closely related, it becomes harder to predict. For instance, an example I come across in my work is this. When I want to classify customer reviews, Speed of App and Speed of delivery classes seem to get confused because they have almost similar keywords in many of their examples. Since they both refer to speed, they are closely related and this leads to the confusion