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 "using honeypots to catch adversarial attacks on neural networks"):
  Abstract: Deep neural networks are known to be vulnerable to adversarial attacks. Numerous efforts have focused on defenses that either try to patch "holes" in trained models, or try to make it difficult or costly to compute adversarial examples that exploit holes. In our work, we explore a counter-intuitive approach of creating a trapdoor-enabled defense. Unlike prior works that try to patch or disguise vulnerable points in the model, we intentionally inject "trapdoors," user-induced weaknesses in the classification manifold that, like honeypots, attract attackers searching for adversarial examples. Attackers' algorithms naturally gravitate towards our trapdoors, leading them to produce attacks that are similar to the trapdoors in the feature space. These are easily identified through similarity of neuron activation signatures compared to those of trapdoors. In this paper, we introduce trapdoors and describe an implementation of a trapdoor-enabled defense. First, we analyze trapdoors as a defense. We prove that not only do trapdoors successfully shape the computation of adversarial attacks, but also that resulting attack inputs will have feature representations very similar to those of inputs with trapdoors. Second, we experimentally show that by protecting models with trapdoors, we can detect, with high accuracy, adversarial examples generated by state-of-the-art attacks (Projected Gradient Descent, optimization-based CW, Elastic Net, BPDA), with negligible impact on the classification of normal inputs. These results generalize across classification domains, including image, facial, and traffic-sign recognition. We also evaluate and validate trapdoors' robustness against multiple strong adaptive attacks.

Includes members:
  Member "defending neural backdoors via generative distribution modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nnoculation broad spectrum and targeted treatment of backdoored dnns")
  Member "stochastic activation pruning for robust adversarial defense":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  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 "are adversarial examples inevitable":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "hardware trojan attacks on neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")
  Member "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")
  Member "feature squeezing detecting adversarial examples in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-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)