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: "Image Captioning and Visual Question Answering Based on Attributes and Their Related External Knowledge."
  Abstract: Much recent progress in Vision-to-Language problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. In this paper we first propose a method of incorporating high-level concepts into the successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering. We further show that the same mechanism can be used to incorporate external knowledge, which is critically important for answering high level visual questions. Specifically, we design a visual question answering model that combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions. It particularly allows questions to be asked about the contents of an image, even when the image itself does not contain a complete answer. Our final model achieves the best reported results on both image captioning and visual question answering on several benchmark datasets.

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"
    - "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 "Revisiting Visual Question Answering Baselines"):
  with members:
    - "An Analysis of Visual Question Answering Algorithms"
    - "Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering"
    - "Deep Attention Neural Tensor Network for Visual Question Answering"

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