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: "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.

Connected through hyperedge HE-1 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models"):
  with members:
    - "strong baseline defenses against clean label poisoning attacks"
    - "anonymousnet natural face de identification with measurable privacy"
    - "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "advhat real world adversarial attack on arcface face id system"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "privacy protective gan for face de identification"

Connected through hyperedge HE-2 (induced by source paper "hidden trigger backdoor attacks"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "gotta catch em all using concealed trapdoors to detect adversarial attacks on neural networks"
    - "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"
    - "poisoning attacks with generative adversarial nets"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-3 (induced by source paper "universal litmus patterns revealing backdoor attacks in cnns"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "gotta catch em all using concealed trapdoors to detect adversarial attacks on neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "certified defenses for data poisoning attacks"
    - "attention is all you need"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"

Connected through hyperedge HE-4 (induced by source paper "invisible backdoor attacks against deep neural networks"):
  with members:
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "is feature selection secure against training data poisoning"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "adversarial diversity and hard positive generation"
    - "membership inference attacks against machine learning models"

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)