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: "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.

Connected through hyperedge HE-1 (induced by source paper "Opinion Mining and Sentiment Analysis"):
  with members:
    - "Using Universal Linguistic Knowledge to Guide Grammar Induction"
    - "Opinion Mining and Information Fusion"
    - "Sentiment analysis in Facebook and its application to e-learning"
    - "Utility scoring of product reviews"
    - "Opinion observer: analyzing and comparing opinions on the Web"
    - "Mining newsgroups using networks arising from social behavior"
    - "User-level sentiment analysis incorporating social networks"
    - "Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior"

Connected through hyperedge HE-2 (induced by source paper "On Calibration of Modern Neural Networks."):
  with members:
    - "Densely Connected Convolutional Networks"
    - "Semantic Reconstruction: Reconstruction of Semantically Segmented 3D Meshes via Volumetric Semantic Fusion"

Connected through hyperedge HE-3 (induced by source paper "Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN"):
  with members:
    - "Learning Representations of Natural Language Texts with Generative Adversarial Networks at Document, Sentence, and Aspect Level"

Connected through hyperedge HE-4 (induced by source paper "The Stanford CoreNLP Natural Language Processing Toolkit"):
  with members:
    - "Information extraction from multi-institutional radiology reports"
    - "Aligning Textual and Graphical Descriptions of Processes Through ILP Techniques"
    - "The price of debiasing automatic metrics in natural language evalaution."
    - "HANS: A Service-Oriented Framework for Chinese Language Processing"

Connected through hyperedge HE-5 (induced by source paper "Relational inductive biases, deep learning, and graph networks."):
  with members:
    - "Neural Machine Translation by Jointly Learning to Align and Translate"
    - "Human-level control through deep reinforcement learning"
    - "Recurrent Models of Visual Attention"
    - "Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning"
    - "Adversarial Attacks on Neural Networks for Graph Data"
    - "A Case for Object Compositionality in Deep Generative Models of Images"

Connected through hyperedge HE-6 (induced by source paper "Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks."):
  with members:
    (none)

Connected through hyperedge HE-7 (induced by source paper "Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction"):
  with members:
    - "Image-Based Recommendations on Styles and Substitutes"
    - "Visual appearance or functional complementarity: Which aspect affects your decision making?"

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