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: "attack graph convolutional networks by adding fake nodes"
  Abstract: Graph convolutional networks (GCNs) have been widely used for classifying graph nodes in the semi-supervised setting. Previous work have shown that GCNs are vulnerable to the perturbation on adjacency and feature matrices of existing nodes. However, it is unrealistic to change existing nodes in many applications, such as existing users in social networks. In this paper, we design algorithms to attack GCNs by adding fake nodes. A greedy algorithm is proposed to generate adjacency and feature matrices of fake nodes, aiming to minimize the classification accuracy on the existing nodes. In additional, we introduce a discriminator to classify fake nodes from real nodes, and propose a Greedy-GAN attack to simultaneously update the discriminator and the attacker, to make fake nodes indistinguishable to the real ones. Our non-targeted attack decreases the accuracy of GCN down to 0.10, and our targeted attack reaches a success rate of 99% on the whole datasets, and 94% on average for attacking a single target node.

Connected through hyperedge HE-1 (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"
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "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"

Connected through hyperedge HE-2 (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"
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "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"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-3 (induced by source paper "unsupervised euclidean distance attack on network embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "ga based q attack on community detection"
    - "data poisoning attack against unsupervised node embedding methods"
    - "target defense against link prediction based attacks via evolutionary perturbations"
    - "hiding individuals and communities in a social network"
    - "synthesizing robust adversarial examples"
    - "node2vec scalable feature learning for networks"
    - "don t walk skip online learning of multi scale network embeddings"

Connected through hyperedge HE-4 (induced by source paper "adversarial examples on graph data deep insights into attack and defense"):
  with members:
    - "simplifying graph convolutional networks"
    - "adversarial attack on graph structured data"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "axiomatic attribution for deep networks"
    - "mitigating adversarial effects through randomization"

Connected through hyperedge HE-5 (induced by source paper "a unified framework for data poisoning attack to graph based semi supervised learning"):
  with members:
    - "adversarial attack on graph structured data"
    - "implicit reparameterization gradients"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "data poisoning attacks against online learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "crafting adversarial input sequences for recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "attacking graph convolutional networks via rewiring"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "adversarial attack on graph structured data"
    - "hierarchical graph representation learning with differentiable pooling"
    - "black box adversarial attacks with limited queries and information"
    - "query efficient hard label black box attack an optimization based approach"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-7 (induced by source paper "time aware gradient attack on dynamic network link prediction"):
  with members:
    - "exact k recommendation via maximal clique optimization"
    - "e lstm d a deep learning framework for dynamic network link prediction"
    - "data poisoning attack against unsupervised node embedding methods"
    - "hiding individuals and communities in a social network"
    - "timers error bounded svd restart on dynamic networks"
    - "semi supervised classification with graph convolutional networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

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)