Basic building blocks of most deep learning models are convolutional layer, pooling layer, fully connected layer, and softmax layer. How do you propose we call "softmax layer" instead?
Well, there are other building blocks, such as batch normalization layer, or local contrast normalization layer (not to mention a dozen of batchnorm alternatives, e.g. group normalization, weight normalization, layer normalization, instance normalization, etc).
If you just say "normalization layer" how am I supposed to know which normalization you're talking about?
This opens up possibility of using something else than softmax in there.