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 "avoiding the man on the wire improving tor s security with trust aware path selection"):
  Abstract: Tor users are vulnerable to deanonymization by an adversary that can observe some Tor relays or some parts of the network. We propose that users use trust to choose paths through Tor that are less likely to be observed. We present a system to model this trust as probability distributions on the location of the user's adversaries. We propose the Trust-Aware Path Selection algorithm for Tor that helps users avoid traffic-analysis attacks while still choosing paths that could have been selected by many other users. We evaluate this algorithm in two settings using a high-level map of Internet routing: (i) users try to avoid a single global adversary that has an independent chance to control each Autonomous System organization, Internet Exchange Point organization, and Tor relay family, and (ii) users try to avoid deanonymization by any single country. We also examine the performance of trust-aware path selection using the Shadow Tor network simulator.

Includes members:
  Member "measuring and mitigating as level adversaries against tor":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepcorr strong flow correlation attacks on tor using deep learning")
  Member "defending tor from network adversaries a case study of network path prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepcorr strong flow correlation attacks on tor using deep learning")
  Member "pisces anonymous communication using social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dandelion lightweight cryptocurrency networking with formal anonymity guarantees")
  Member "stealthy traffic analysis of low latency anonymous communication using throughput fingerprinting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "peel the onion recognition of android apps behind the tor network")

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)