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: "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.

Connected through hyperedge HE-1 (induced by source paper "revisiting and evaluating software side channel vulnerabilities and countermeasures in cryptographic applications"):
  with members:
    - "when human cognitive modeling meets pins user independent inter keystroke timing attacks"
    - "identifying cache based side channels through secret augmented abstract interpretation"
    - "sgxpectre stealing intel secrets from sgx enclaves via speculative execution"
    - "spoiler speculative load hazards boost rowhammer and cache attacks"
    - "meltdownprime and spectreprime automatically synthesized attacks exploiting invalidation based coherence protocols"
    - "verifying and synthesizing constant resource implementations with types"
    - "spectre attacks exploiting speculative execution"
    - "a systematic evaluation of transient execution attacks and defenses"

Connected through hyperedge HE-2 (induced by source paper "magneticspy exploiting magnetometer in mobile devices for website and application fingerprinting"):
  with members:
    - "stealing pins via mobile sensors actual risk versus user perception"
    - "magneto covert channel between air gapped systems and nearby smartphones via cpu generated magnetic fields"
    - "perfweb how to violate web privacy with hardware performance events"
    - "a study on power side channels on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "packet chasing spying on network packets over a cache side channel"):
  with members:
    - "flush flush a fast and stealthy cache attack"
    - "flush flush a fast and stealthy cache attack"

Connected through hyperedge HE-4 (induced by source paper "mi6 secure enclaves in a speculative out of order processor"):
  with members:
    - "sanctorum a lightweight security monitor for secure enclaves"

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)