You could always correlate the existing weights to the existing classes in the dataset and wipe the lowest-N correlated weights from each layer while adding a new output with new weights. this could catastrophically impact performance but also would guarantee you minimize impact on existing classes ...

I work with AI but can't guarantee this works since you have no notion of how weights earlier in the network impact latter layers....