human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups authors who co-authored the same paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Neural Machine Translation by Jointly Learning to Align and Translate":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Maxout Networks")
  Member "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Opinion Mining and Sentiment Analysis")
  Member "Human-level control through deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning Scheduling Algorithms for Data Processing Clusters.")
  Member "Recurrent Models of Visual Attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Segmentation as selective search for object recognition")
  Member "Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Human-level control through deep reinforcement learning")
  Member "Adversarial Attacks on Neural Networks for Graph Data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Adversarial Attack on Graph Structured Data")
  Member "A Case for Object Compositionality in Deep Generative Models of Images":
    also connected through other hyperedges:
      (none)

Question: Please tell me which research area the co-authored paper (i.e., the source paper of the center hyperedge) belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Mathematics