You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: When the number of classes is very large, it makes sense for thecumbersome model to be an ensemble that contains one generalistmodel trained on all the data and many “specialist”models, each of which is trained on data that is highly enriched inexamples from a very confusable subset of the classes (like differenttypes of mushroom). The softmax of this type of specialist can be mademuch smaller by combining all of the classes it does not care about into asingle dustbin class. The specialists that we used in our experiments on the JFT datasetcollapsed all of their non-specialist classes into a single dustbinclass. If we allow specialists to have a full softmax over allclasses, there may be a much better way to prevent them overfitting than usingearly stopping. A specialist is trained on data that is highlyenriched in its special classes. This means that the effective sizeof its training set is much smaller and it has a strong tendency tooverfit on its special classes. This problem cannot be solved bymaking the specialist a lot smaller because then we lose the veryhelpful transfer effects we get from modeling all of thenon-specialist classes.

Question: When discussing the JFT specialist training, the authors refer to a "dustbin" class. Give an example of a sample that might be assigned to this class.
The dustbin class was the combination of all of the classes that a specialist does not care about. Examples of samples that might be assigned to the dustbin class cannot be provided from this paper.