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: "Adversarial Attacks on Neural Networks for Graph Data"
  Abstract: Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g. the web, adversaries are common. Can deep learning models for graphs be easily fooled? In this work, we introduce the first study of adversarial attacks on attributed graphs, specifically focusing on models exploiting ideas of graph convolutions. In addition to attacks at test time, we tackle the more challenging class of poisoning/causative attacks, which focus on the training phase of a machine learning model.We generate adversarial perturbations targeting the node's features and the graph structure, thus, taking the dependencies between instances in account. Moreover, we ensure that the perturbations remain unnoticeable by preserving important data characteristics. To cope with the underlying discrete domain we propose an efficient algorithm Nettack exploiting incremental computations. Our experimental study shows that accuracy of node classification significantly drops even when performing only few perturbations. Even more, our attacks are transferable: the learned attacks generalize to other state-of-the-art node classification models and unsupervised approaches, and likewise are successful even when only limited knowledge about the graph is given.

Connected through hyperedge HE-1 (induced by source paper "Adversarial Attack on Graph Structured Data"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Relational inductive biases, deep learning, and graph networks."):
  with members:
    - "Neural Machine Translation by Jointly Learning to Align and Translate"
    - "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank"
    - "Human-level control through deep reinforcement learning"
    - "Recurrent Models of Visual Attention"
    - "Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning"
    - "A Case for Object Compositionality in Deep Generative Models of Images"

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