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 "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank"):
  Abstract: Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases.

Includes members:
  Member "The Stanford CoreNLP Natural Language Processing Toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Aligning Textual and Graphical Descriptions of Processes Through ILP Techniques")
  Member "Opinion Mining and Sentiment Analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Deep Sparse Rectifier Neural Networks")
  Member "Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Visual appearance or functional complementarity: Which aspect affects your decision making?")
  Member "Relational inductive biases, deep learning, and graph networks.":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Neural Machine Translation by Jointly Learning to Align and Translate")
  Member "Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Learning Representations of Natural Language Texts with Generative Adversarial Networks at Document, Sentence, and Aspect Level")
  Member "On Calibration of Modern Neural Networks.":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Densely Connected Convolutional Networks")
  Member "Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks.":
    also connected through other hyperedges:
      (none)
  Member "The Good, The Bad & The Ugly Features: A Meta-analysis on User Review About Food Journaling Apps":
    also connected through other hyperedges:
      (none)

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