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 "blind adversarial network perturbations"):
  Abstract: Deep Neural Networks (DNNs) are commonly used for various traffic analysis problems, such as website fingerprinting and flow correlation, as they outperform traditional (e.g., statistical) techniques by large margins. However, deep neural networks are known to be vulnerable to adversarial examples: adversarial inputs to the model that get labeled incorrectly by the model due to small adversarial perturbations. In this paper, for the first time, we show that an adversary can defeat DNN-based traffic analysis techniques by applying \emph{adversarial perturbations} on the patterns of \emph{live} network traffic.

Includes members:
  Member "universal adversarial audio perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "active subspace of neural networks structural analysis and universal attacks")
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")
  Member "deep fingerprinting undermining website fingerprinting defenses with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial network traffic toward evaluating the robustness of deep learning based network traffic classification")
  Member "deepcorr strong flow correlation attacks on tor using deep learning":
    also connected through other hyperedges:
      (none)
  Member "automated website fingerprinting through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial network traffic toward evaluating the robustness of deep learning based network traffic classification")
  Member "var cnn and dynaflow improved attacks and defenses for website fingerprinting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a federated learning approach for mobile packet classification")
  Member "adversarial examples are not easily detected bypassing ten detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)