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 examples on graph data deep insights into attack and defense"
  Abstract: Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep learning models often suffer from adversarial attacks. However, compared with non-graph data, the discrete features, graph connections and different definitions of imperceptible perturbations bring unique challenges and opportunities for the adversarial attacks and defenses for graph data. In this paper, we propose both attack and defense techniques. For attack, we show that the discreteness problem could easily be resolved by introducing integrated gradients which could accurately reflect the effect of perturbing certain features or edges while still benefiting from the parallel computations. For defense, we observe that the adversarially manipulated graph for the targeted attack differs from normal graphs statistically. Based on this observation, we propose a defense approach which inspects the graph and recovers the potential adversarial perturbations. Our experiments on a number of datasets show the effectiveness of the proposed methods.

Connected through hyperedge HE-1 (induced by source paper "transferring robustness for graph neural network against poisoning attacks"):
  with members:
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "graph neural networks with generated parameters for relation extraction"
    - "graph few shot learning via knowledge transfer"
    - "graph convolutional networks with eigenpooling"
    - "graph neural networks with high order feature interactions"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"

Connected through hyperedge HE-2 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study"):
  with members:
    - "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"
    - "adversarial attack on community detection by hiding individuals"
    - "transferring robustness for graph neural network against poisoning attacks"
    - "graph universal adversarial attacks a few bad actors ruin graph learning models"
    - "adversarial training methods for network embedding"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "the general black box attack method for graph neural networks"
    - "graphsac detecting anomalies in large scale graphs"

Connected through hyperedge HE-3 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness to graph perturbations"
    - "attacking graph based classification via manipulating the graph structure"
    - "adversarial attack on graph structured data"

Connected through hyperedge HE-4 (induced by source paper "certifiable robustness to graph perturbations"):
  with members:
    - "transferring robustness for graph neural network against poisoning attacks"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial robustness of similarity based link prediction"
    - "adversarial defense framework for graph neural network"
    - "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-5 (induced by source paper "graphdefense towards robust graph convolutional networks"):
  with members:
    - "adversarial defense framework for graph neural network"
    - "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-6 (induced by source paper "edge dithering for robust adaptive graph convolutional networks"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial attack on graph structured data"
    - "adaptive graph convolutional neural networks"
    - "how powerful are graph neural networks"
    - "predicting multicellular function through multi layer tissue networks"
    - "geometric deep learning going beyond euclidean data"
    - "semi supervised classification with graph convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "node injection attacks on graphs via reinforcement learning"):
  with members:
    - "adversarial policies attacking deep reinforcement learning"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "adversarial attack on graph structured data"
    - "is feature selection secure against training data poisoning"
    - "graph transformation policy network for chemical reaction prediction"
    - "real time bidding by reinforcement learning in display advertising"
    - "semi supervised classification with graph convolutional networks"
    - "discriminative embeddings of latent variable models for structured data"

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)