human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank"):
  with members:
    - "The Stanford CoreNLP Natural Language Processing Toolkit"
    - "Opinion Mining and Sentiment Analysis"
    - "Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction"
    - "Relational inductive biases, deep learning, and graph networks."
    - "Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN"
    - "On Calibration of Modern Neural Networks."
    - "The Good, The Bad & The Ugly Features: A Meta-analysis on User Review About Food Journaling Apps"
    - "Learning Topic-Oriented Word Embedding for Query Classification"

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science