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 "adv dwf defending against deep learning based website fingerprinting attacks with adversarial traces"):
  Abstract: Website Fingerprinting (WF) is a type of traffic analysis attack that enables a local passive eavesdropper to infer the victim's activity even when the traffic is protected by encryption, a VPN, or some other anonymity system like Tor. Leveraging a deep-learning classifier, a WF attacker can gain up to 98% accuracy against Tor. Existing WF defenses are either too expensive in terms of bandwidth and latency overheads (e.g. 2-3 times as large or slow) or ineffective against the latest attacks. In this paper, we explore a novel defense, Adv-DWF, based on the idea of adversarial examples that have been shown to undermine machine learning classifiers in other domains. Our Adv-DWF defense adds padding to a traffic trace in a manner that fools the classifier into classifying it as coming from a different site. The technique drops the accuracy of the state-of-the-art attack augmented with adversarial training from 98% to 35%, while incurring a reasonable 56% bandwidth overhead. For most of the cases, the state-of-the-art attack's accuracies of our defense are at least 45% and 14% lower than state-of-the-art defenses WTF-PAD and Walkie-Talkie (W-T), respectively. The Top-2 accuracy of our defense is at best 56.9%, while it is over 98% for W-T. In addition, for the most cases, the bandwidth overheads of our defense are at least 8% and 6% lower than WTF-PAD and W-T, respectively, showing its promise as a possible defense for Tor.

Includes members:
  Member "measuring information leakage in website fingerprinting attacks and defenses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces")
  Member "deep fingerprinting undermining website fingerprinting defenses with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blind adversarial network perturbations")
  Member "automated website fingerprinting through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blind adversarial network perturbations")
  Member "certified defenses against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  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 "bayes not naive security bounds on website fingerprinting defenses":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces")

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)