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 "tik tok the utility of packet timing in website fingerprinting attacks"):
  Abstract: A passive local eavesdropper can leverage Website Fingerprinting (WF) to deanonymize the web browsing activity of Tor users. The value of timing information to WF has often been discounted in recent works due to the volatility of low-level timing information. In this paper, we more carefully examine the extent to which packet timing can be used to facilitate WF attacks. We first propose a new set of timing-related features based on burst-level characteristics to further identify more ways that timing patterns could be used by classifiers to identify sites. Then we evaluate the effectiveness of both raw timing and directional timing which is a combination of raw timing and direction in a deep-learning-based WF attack. Our closed-world evaluation shows that directional timing performs best in most of the settings we explored, achieving: (i) 98.4% in undefended Tor traffic; (ii) 93.5% on WTF-PAD traffic, several points higher than when only directional information is used; and (iii) 64.7% against onion sites, 12% higher than using only direction. Further evaluations in the open-world setting show small increases in both precision (+2%) and recall (+6%) with directional-timing on WTF-PAD traffic. To further investigate the value of timing information, we perform an information leakage analysis on our proposed handcrafted features. Our results show that while timing features leak less information than directional features, the information contained in each feature is mutually exclusive to one another and can thus improve the robustness of a classifier.

Includes members:
  Member "var cnn a data efficient website fingerprinting attack based on deep learning":
    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 "measuring information leakage in website fingerprinting attacks and defenses":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "blind adversarial network perturbations")
  Member "techniques for interpretable machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "understanding tor usage with privacy preserving measurement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "are anonymity seekers just like everybody else an analysis of contributions to wikipedia from tor")
  Member "automated website fingerprinting through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blind adversarial network perturbations")
  Member "how unique is your onion an analysis of the fingerprintability of tor onion services":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "encrypted dns privacy a traffic analysis perspective")
  Member "k fingerprinting a robust scalable website fingerprinting technique":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blind adversarial network perturbations")

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)