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: "Automatic Conversational Helpdesk Solution using Seq2Seq and Slot-filling Models"
  Abstract: Helpdesk is a key component of any large IT organization, where users can log a ticket about any issue they face related to IT infrastructure, administrative services, human resource services, etc. Normally, users have to assign appropriate set of labels to a ticket so that it could be routed to right domain expert who can help resolve the issue. In practice, the number of labels are very large and organized in form of a tree. It is non-trivial to describe the issue completely and attach appropriate labels unless one knows the cause of the problem and the related labels. Sometimes domain experts discuss the issue with the users and change the ticket labels accordingly, without modifying the ticket description. This results in inconsistent and badly labeled data, making it hard for supervised algorithms to learn from. In this paper, we propose a novel approach of creating a conversational helpdesk system, which will ask relevant questions to the user, for identification of the right category and will then raise a ticket on users' behalf. We use attention based seq2seq model to assign the hierarchical categories to tickets. We use a slot filling model to help us decide what questions to ask to the user, if the top-k model predictions are not consistent. We also present a novel approach to generate training data for the slot filling model automatically based on attention in the hierarchical classification model. We demonstrate via a simulated user that the proposed approach can give us a significant gain in accuracy on ticket-data without asking too many questions to users. Finally, we also show that our seq2seq model is as versatile as other approaches on publicly available datasets, as state of the art approaches.

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"
    - "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"
    - "MEgo2Vec: Embedding Matched Ego Networks for User Alignment Across Social Networks"

Connected through hyperedge HE-2 (induced by source paper "A Neural Conversational Model"):
  with members:
    - "Neural Machine Translation by Jointly Learning to Align and Translate"
    - "Exploring the limits of language modeling"
    - "How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation"

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