human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "adversarial defense framework for graph neural network"
  Abstract: Graph neural network (GNN), as a powerful representation learning model on graph data, attracts much attention across various disciplines. However, recent studies show that GNN is vulnerable to adversarial attacks. How to make GNN more robust? What are the key vulnerabilities in GNN? How to address the vulnerabilities and defense GNN against the adversarial attacks? In this paper, we propose DefNet, an effective adversarial defense framework for GNNs. In particular, we first investigate the latent vulnerabilities in every layer of GNNs and propose corresponding strategies including dual-stage aggregation and bottleneck perceptron. Then, to cope with the scarcity of training data, we propose an adversarial contrastive learning method to train the GNN in a conditional GAN manner by leveraging the high-level graph representation. Extensive experiments on three public datasets demonstrate the effectiveness of DefNet in improving the robustness of popular GNN variants, such as Graph Convolutional Network and GraphSAGE, under various types of adversarial attacks.

Connected through hyperedge HE-1 (induced by source paper "certifiable robustness to graph perturbations"):
  with members:
    - "transferring robustness for graph neural network against poisoning attacks"
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial robustness of similarity based link prediction"
    - "simplifying graph convolutional networks"
    - "learning graph neural networks with noisy labels"
    - "fast graph representation learning with pytorch geometric"
    - "adversarial attacks on graph neural networks via meta learning"

Connected through hyperedge HE-2 (induced by source paper "graphdefense towards robust graph convolutional networks"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "adversarial attacks on graph neural networks via meta learning"
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-3 (induced by source paper "heterogeneous graph matching networks"):
  with members:
    - "medgcn graph convolutional networks for multiple medical tasks"
    - "a comprehensive survey on graph neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "neural message passing for quantum chemistry"
    - "burstiness of intrusion detection process empirical evidence and a modeling approach"
    - "entity embedding based anomaly detection for heterogeneous categorical events"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.LG(Machine Learning)