In higher education, educational resources are the vessel with which information get transferred to the learner. Information on the content discussed in the scope of the educational resources, however, is implicit and must be inferred by the user by reading the resource title or through contextual information. In this paper we present a state-of-the-art neural natural language processing system, based on Google-BERT, that maps educational resource titles into one of 905 classes from the Dewey Decimal Classification (DDC) system. We present model architecture, training procedure dataset properties and our performance analysis methodology. We show that aside from classification performance, our model implicitly learns the class hierarchy inherent to the DDC.