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: "Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction"
  Abstract: Recently, many e-commerce websites have encouraged their users to rate shopping items and write review texts. This review information has been very useful for understanding user preferences and item properties, as well as enhancing the capability to make personalized recommendations of these websites. In this paper, we propose to model user preferences and item properties using convolutional neural networks (CNNs) with dual local and global attention, motivated by the superiority of CNNs to extract complex features. By using aggregated review texts from a user and aggregated review text for an item, our model can learn the unique features (embedding) of each user and each item. These features are then used to predict ratings. We train these user and item networks jointly which enable the interaction between users and items in a similar way as matrix factorization. The local attention provides us insight on a user's preferences or an item's properties. The global attention helps CNNs focus on the semantic meaning of the whole review text. Thus, the combined local and global attentions enable an interpretable and better-learned representation of users and items. We validate the proposed models by testing on popular review datasets in  Yelp  and  Amazon  and compare the results with matrix factorization (MF), the hidden factor and topical (HFT) model, and the recently proposed convolutional matrix factorization (ConvMF+). Our proposed CNNs with dual attention model outperforms HFT and ConvMF+ in terms of mean square errors (MSE). In addition, we compare the user/item embeddings learned from these models for classification and recommendation. These results also confirm the superior quality of user/item embeddings learned from our model.

Connected through hyperedge HE-1 (induced by source paper "Visual appearance or functional complementarity: Which aspect affects your decision making?"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Image-Based Recommendations on Styles and Substitutes"):
  with members:
    - "Personalized recommendation on dynamic content using predictive bilinear models"
    - "CRPP: Competing Recurrent Point Process for Modeling Visibility Dynamics in Information Diffusion"

Connected through hyperedge HE-3 (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"
    - "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."
    - "Ordered Neurons: Integrating Tree Structures into Recurrent 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