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: "power up robust graph convolutional network against evasion attacks based on graph powering"
  Abstract: Graph convolutional networks (GCNs) are powerful tools for graph-structured data. However, they have been recently shown to be prone to topological attacks. Despite substantial efforts to search for new architectures, it still remains a challenge to improve performance in both benign and adversarial situations simultaneously. In this paper, we re-examine the fundamental building block of GCN---the Laplacian operator---and highlight some basic flaws in the spatial and spectral domains. As an alternative, we propose an operator based on graph powering, and prove that it enjoys a desirable property of "spectral separation." Based on the operator, we propose a robust learning paradigm, where the network is trained on a family of "'smoothed" graphs that span a spatial and spectral range for generalizability. We also use the new operator in replacement of the classical Laplacian to construct an architecture with improved spectral robustness, expressivity and interpretability. The enhanced performance and robustness are demonstrated in extensive experiments.

Connected through hyperedge HE-1 (induced by source paper "transferring robustness for graph neural network against poisoning attacks"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "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 "a survey of adversarial learning on graphs"):
  with members:
    - "heterogeneous graph neural networks for malicious account detection"
    - "dynamic graph convolutional networks"
    - "multiscale evolutionary perturbation attack on community detection"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "time aware gradient attack on dynamic network link prediction"
    - "a restricted black box adversarial framework towards attacking graph embedding models"
    - "adversarial robustness of similarity based link prediction"
    - "graphsac detecting anomalies in large scale graphs"

Connected through hyperedge HE-3 (induced by source paper "spectral graph attention network"):
  with members:
    - "stabilizing gans with octave convolutions"
    - "a restricted black box adversarial framework towards attacking graph embedding models"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "graph neural networks with convolutional arma filters"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "semi supervised graph classification a hierarchical graph perspective"
    - "graph wavelet neural network"

Connected through hyperedge HE-4 (induced by source paper "graph universal adversarial attacks a few bad actors ruin graph learning models"):
  with members:
    - "session based recommendation with graph neural network"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial attacks on graph neural networks via meta learning"
    - "universal transferable and targeted adversarial attacks"
    - "adversarial attack on graph structured data"
    - "adversarial patch"
    - "attack graph convolutional networks by adding fake nodes"
    - "neural message passing for quantum chemistry"

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)