human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups authors who co-authored the same paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks."):
  Abstract: In general, natural language is governed by a tree structure: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). This is a strict hierarchy: when a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM allows different neurons to track information at different time scales, the architecture does not impose a strict hierarchy. This paper proposes to add such a constraint to the system by ordering the neurons; a vector of "master" input and forget gates ensure that when a given unit is updated, all of the units that follow it in the ordering are also updated. To this end, we propose a new RNN unit: ON-LSTM, which achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.

Includes members:
  Member "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Opinion Mining and Sentiment Analysis")

Question: Please tell me which research area the co-authored paper (i.e., the source paper of the center hyperedge) belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science