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 "robust website fingerprinting through the cache occupancy channel"):
  Abstract: Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic traveling between the user's computer and the Tor network. In this work we investigate these attacks under a different attack model, in which the adversary is capable of running a small amount of unprivileged code on the target user's computer. Under this model, the attacker can mount cache side-channel attacks, which exploit the effects of contention on the CPU's cache, to identify the website being browsed. In an important special case of this attack model, a JavaScript attack is launched when the target user visits a website controlled by the attacker. The effectiveness of this attack scenario has never been systematically analyzed, especially in the open-world model which assumes that the user is visiting a mix of both sensitive and non-sensitive sites. In this work we show that cache website fingerprinting attacks in JavaScript are highly feasible, even when they are run from highly restrictive environments, such as the Tor Browser. Specifically, we use machine learning techniques to classify traces of cache activity. Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last-level cache. We show that our approach achieves high classification accuracy in both the open-world and the closed-world models. We further show that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the Spectre attack.

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 "spectre attacks exploiting speculative execution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards probabilistic verification of machine unlearning")
  Member "automated website fingerprinting through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blind adversarial network perturbations")
  Member "loophole timing attacks on shared event loops in chrome":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting and evaluating software side channel vulnerabilities and countermeasures in cryptographic applications")
  Member "software grand exposure sgx cache attacks are practical":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed systems and trusted execution environments trade offs and challenges")
  Member "perfweb how to violate web privacy with hardware performance events":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "jackhammer efficient rowhammer on heterogeneous fpga cpu platforms")
  Member "a software approach to defeating side channels in last level caches":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a compiler assisted scheduler for detecting and mitigating cache based side channel attacks")
  Member "inferring fine grained control flow inside sgx enclaves with branch shadowing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "copycat controlled instruction level attacks on enclaves for maximal key extraction")

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)