human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "adversarial attack on graph structured data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "graph convolutional neural networks for web scale recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "black box generation of adversarial text sequences to evade deep learning classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "deep text classification can be fooled":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robustness verification for transformers")
  Member "fastgcn fast learning with graph convolutional networks via importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectral graph attention network")
  Member "query efficient hard label black box attack an optimization based approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "towards crafting text adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")

Question: Please tell me which class the source paper 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)