Multi-class classification v.s. Binary classification
The greater the number of output nodes the higher complexity you will add to your model. This means that given a fixed amount of data, a greater number of output nodes will lead to poorer results. I would use a ABCD vs. others strategy.

Instead of conditioning your model to learn the distributions of the class A, B, C and D separately you will combine them. This means that is A and B are different in some way, but this difference is irrespective of the classification with "others" then there is no need to learn that distinction.

For example: if you want to detect dog, cat, human with features such as weight, height and number of legs. The number of legs feature will have relatively low importance, because cats and dogs will likely all have 4 legs. However if I want to classify cat/dog vs humans, then the number of legs will be the most important feature. It might be the only feature you need.

One caveat may be severe class imbalance. By combining your classes in this way you may end up with an over representation of ABCD. You can use techniques such as anomaly detection to train a model on your ABCD data and then detect whether a novel instance falls within this distribution, or is an outlier in which case you would label it as "others."