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 "trust but verify an information theoretic explanation for the adversarial fragility of machine learning systems and a general defense against adversarial attacks"):
  Abstract: Deep-learning based classification algorithms have been shown to be susceptible to adversarial attacks: minor changes to the input of classifiers can dramatically change their outputs, while being imperceptible to humans. In this paper, we present a simple hypothesis about a feature compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations. Drawing on ideas from information and coding theory, we propose a general class of defenses for detecting classifier errors caused by abnormally small input perturbations. We further show theoretical guarantees for the performance of this detection method. We present experimental results with (a) a voice recognition system, and (b) a digit recognition system using the MNIST database, to demonstrate the effectiveness of the proposed defense methods. The ideas in this paper are motivated by a simple analogy between AI classifiers and the standard Shannon model of a communication system.

Includes members:
  Member "discretization based solutions for secure machine learning against adversarial attacks":
    also connected through other hyperedges:
      (none)
  Member "parsimonious black box adversarial attacks via efficient combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provably robust boosted decision stumps and trees against adversarial attacks")
  Member "jumprelu a retrofit defense strategy for adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")
  Member "robust neural networks using randomized adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")
  Member "weight map layer for noise and adversarial attack robustness":
    also connected through other hyperedges:
      (none)
  Member "efficient decision based black box adversarial attacks on face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "toward adversarial robustness via semi supervised robust training")
  Member "zk gandef a gan based zero knowledge adversarial training defense for neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using single step adversarial training to defend iterative adversarial examples")
  Member "simple black box adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "black box smoothing a provable defense for pretrained classifiers")

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.CR(Cryptography and Security)