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: "identifying and characterizing sybils in the tor network"
  Abstract: Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack practical tools and methods to expose Sybil attacks. In this work, we develop sybilhunter, the first system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used sybilhunter's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attackers. Our findings include diverse Sybils, ranging from botnets, to academic research, and relays that hijack Bitcoin transactions. Our work shows that existing Sybil defenses do not apply to Tor, it delivers insights into real-world attacks, and provides practical tools to uncover and characterize Sybils, making the network safer for its users.

Connected through hyperedge HE-1 (induced by source paper "how do tor users interact with onion services"):
  with members:
    - "genuine onion simple fast flexible and cheap website authentication"

Connected through hyperedge HE-2 (induced by source paper "honey onions a framework for characterizing and identifying misbehaving tor hsdirs"):
  with members:
    - "raptor routing attacks on privacy in tor"
    - "onionbots subverting privacy infrastructure for cyber attacks"
    - "content and popularity analysis of tor hidden services"
    - "traveling the silk road a measurement analysis of a large anonymous online marketplace"

Connected through hyperedge HE-3 (induced by source paper "safety in numbers anonymization makes centralized systems trustworthy"):
  with members:
    - "enhancing bitcoin security and performance with strong consistency via collective signing"
    - "keeping authorities honest or bust with decentralized witness cosigning"

Connected through hyperedge HE-4 (induced by source paper "verifying public keys without trust how anonymity can guarantee data integrity"):
  with members:
    - "enhancing bitcoin security and performance with strong consistency via collective signing"
    - "keeping authorities honest or bust with decentralized witness cosigning"

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)