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: "bayes not naive security bounds on website fingerprinting defenses"
  Abstract: Website Fingerprinting (WF) attacks raise major concerns about users' privacy. They employ Machine Learning (ML) to allow a local passive adversary to uncover the Web browsing behavior of a user, even if she browses through an encrypted tunnel (e.g. Tor, VPN). Numerous defenses have been proposed in the past; however, it is typically difficult to have formal guarantees on their security, which is most often evaluated empirically against state-of-the-art attacks. In this paper, we present a practical method to derive security bounds for any WF defense, which depend on a chosen feature set. This result derives from reducing WF attacks to an ML classification task, where we can determine the smallest achievable error (the Bayes error); such error can be estimated in practice, and is a lower bound for a WF adversary, for any classification algorithm he may use. Our work has two main consequences: i) it allows determining the security of WF defenses, in a black-box manner, with respect to the state-of-the-art feature set and ii) it favors shifting the focus of future WF research to the identification of optimal feature sets. The generality of the approach further suggests that the method could be used to define security bounds for other ML-based attacks.

Connected through hyperedge HE-1 (induced by source paper "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"):
  with members:
    - "var cnn a data efficient website fingerprinting attack based on deep learning"
    - "measuring information leakage in website fingerprinting attacks and defenses"
    - "tik tok the utility of packet timing in website fingerprinting attacks"
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "automated website fingerprinting through deep learning"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-2 (induced by source paper "adv dwf defending against deep learning based website fingerprinting attacks with adversarial traces"):
  with members:
    - "measuring information leakage in website fingerprinting attacks and defenses"
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "automated website fingerprinting through deep learning"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-3 (induced by source paper "f bleau fast black box leakage estimation"):
  with members:
    - "mine mutual information neural estimation"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "membership inference attacks against machine learning models"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "geo indistinguishability differential privacy for location based systems"
    - "on an improvement over renyi s equivocation bound"

Connected through hyperedge HE-4 (induced by source paper "measuring information leakage in website fingerprinting attacks and defenses"):
  with members:
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "the effect of dns on tor s anonymity"
    - "k fingerprinting a robust scalable website fingerprinting technique"
    - "toward an efficient website fingerprinting defense"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "comparing bayesian network classifiers"

Connected through hyperedge HE-5 (induced by source paper "evading classifiers in discrete domains with provable optimality guarantees"):
  with members:
    - "pots protective optimization technologies"
    - "scaling provable adversarial defenses"
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "cost sensitive robustness against adversarial examples"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"

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.CR(Cryptography and Security)