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: "MEgo2Vec: Embedding Matched Ego Networks for User Alignment Across Social Networks"
  Abstract: Aligning users across multiple heterogeneous social networks is a fundamental issue in many data mining applications. Methods that incorporate user attributes and network structure have received much attention. However, most of them suffer from error propagation or the noise from diverse neighbors in the network. To effectively model the influence from neighbors, we propose a graph neural network to directly represent the ego networks of two users to be aligned into an embedding, based on which we predict the alignment label. Three major mechanisms in the model are designed to unitedly represent different attributes, distinguish different neighbors and capture the structure information of the ego networks respectively.   Systematically, we evaluate the proposed model on a number of academia and social networking datasets with collected alignment labels. Experimental results show that the proposed model achieves significantly better performance than the state-of-the-art comparison methods (+3.12-30.57% in terms of F1 score).

Connected through hyperedge HE-1 (induced by source paper "Neural Machine Translation by Jointly Learning to Align and Translate"):
  with members:
    - "A Neural Conversational Model"
    - "Automatic Conversational Helpdesk Solution using Seq2Seq and Slot-filling Models"
    - "Maxout Networks"
    - "Image Captioning and Visual Question Answering Based on Attributes and Their Related External Knowledge."
    - "Relational inductive biases, deep learning, and graph networks."
    - "Identifying the Relative Importance of Customer Issues on Product Ratings through Machine Learning"
    - "CanvasGAN: A simple baseline for text to image generation by incrementally patching a canvas"
    - "Generating Keyword Queries for Natural Language Queries to Alleviate Lexical Chasm Problem"

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