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: "Relational inductive biases, deep learning, and graph networks."
  Abstract: Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI. 
The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between "hand-engineering" and "end-to-end" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.

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."
    - "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 "Adversarial Attacks on Neural Networks for Graph Data"):
  with members:
    - "Adversarial Attack on Graph Structured Data"

Connected through hyperedge HE-3 (induced by source paper "Recurrent Models of Visual Attention"):
  with members:
    - "Segmentation as selective search for object recognition"
    - "Neural self-compressor: Collective interpretation by compressing multi-layered neural networks into non-layered networks"
    - "Rao-Blackwellized Stochastic Gradients for Discrete Distributions."

Connected through hyperedge HE-4 (induced by source paper "A Case for Object Compositionality in Deep Generative Models of Images"):
  with members:
    (none)

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "Human-level control through deep reinforcement learning"):
  with members:
    - "An object-oriented representation for efficient reinforcement learning"
    - "Cooperative and Competitive Reinforcement and Imitation Learning for a Mixture of Heterogeneous Learning Modules"
    - "Learning Scheduling Algorithms for Data Processing Clusters."
    - "Hierarchical Information Entropy System Model for TWfMS"
    - "S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning."
    - "Graph Convolutional Reinforcement Learning for Multi-Agent Cooperation."
    - "Stochastic Substitute Training: A Gray-box Approach to Craft Adversarial Examples Against Gradient Obfuscation Defenses"
    - "Evolving Robust Policy Coverage Sets in Multi-Objective Markov Decision Processes Through Intrinsically Motivated Self-Play"

Connected through hyperedge HE-7 (induced by source paper "Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning"):
  with members:
    - "Human-level control through deep reinforcement learning"

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: Mathematics