How to change/adapt loss function while using "class" incremental learning

As a beginner on class incremental learning and trying to understand the general concept. In class incremental learning, we have a model that can make a classification between classes A, B, and C. By using data from another class D, we want to apply class incremental learning to obtain a model which can predict classes among A, B, C and D without training all data with all classes from scratch.

My question is, since our softmax (that we use to calculate loss (cross-entropy loss)) has a fixed size (3 in case of A, B, and C classes), how this can be possible? Do we change the loss in class incremental learning? How to deal with that?

Note 1: The video I watched to get an idea about class incremental learning is iCaRL- incremental Classifier and Representation Learning.

Note 2: I saw the catastrophic forgetting issue, but my question does not cover this. It is (I suppose) more straightforward.