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: "Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN"
  Abstract: A divide-and-conquer method classifying sentence types before sentiment analysis.Classifying sentence types by the number of opinion targets a sentence contain.A data-driven approach automatically extract features from input sentences. Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.

Connected through hyperedge HE-1 (induced by source paper "Learning Representations of Natural Language Texts with Generative Adversarial Networks at Document, Sentence, and Aspect Level"):
  with members:
    (none)

Connected through hyperedge HE-2 (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."
    - "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