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 "understanding the one pixel attack propagation maps and locality analysis"):
  Abstract: Deep neural networks were shown to be vulnerable to single pixel modifications. However, the reason behind such phenomena has never been elucidated. Here, we propose Propagation Maps which show the influence of the perturbation in each layer of the network. Propagation Maps reveal that even in extremely deep networks such as Resnet, modification in one pixel easily propagates until the last layer. In fact, this initial local perturbation is also shown to spread becoming a global one and reaching absolute difference values that are close to the maximum value of the original feature maps in a given layer. Moreover, we do a locality analysis in which we demonstrate that nearby pixels of the perturbed one in the one-pixel attack tend to share the same vulnerability, revealing that the main vulnerability lies in neither neurons nor pixels but receptive fields. Hopefully, the analysis conducted in this work together with a new technique called propagation maps shall shed light into the inner workings of other adversarial samples and be the basis of new defense systems to come.

Includes members:
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "adversarial patch":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")
  Member "characterizing adversarial subspaces using local intrinsic dimensionality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "feature squeezing detecting adversarial examples in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "adversarial examples are not easily detected bypassing ten detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "synthesizing robust adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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)