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: "defending tor from network adversaries a case study of network path prediction"
  Abstract: The Tor anonymity network has been shown vulnerable to traffic analysis attacks by autonomous systems and Internet exchanges, which can observe different overlay hops belonging to the same circuit. We aim to determine whether network path prediction techniques provide an accurate picture of the threat from such adversaries, and whether they can be used to avoid this threat. We perform a measurement study by running traceroutes from Tor relays to destinations around the Internet. We use the data to evaluate the accuracy of the autonomous systems and Internet exchanges that are predicted to appear on the path using state-of-the-art path inference techniques; we also consider the impact that prediction errors have on Tor security, and whether it is possible to produce a useful overestimate that does not miss important threats. Finally, we evaluate the possibility of using these predictions to actively avoid AS and IX adversaries and the challenges this creates for the design of Tor.

Connected through hyperedge HE-1 (induced by source paper "deepcorr strong flow correlation attacks on tor using deep learning"):
  with members:
    - "automated website fingerprinting through deep learning"
    - "counter raptor safeguarding tor against active routing attacks"
    - "tensorflow a system for large scale machine learning"
    - "raptor routing attacks on privacy in tor"
    - "measuring and mitigating as level adversaries against tor"
    - "k fingerprinting a robust scalable website fingerprinting technique"
    - "adam a method for stochastic optimization"
    - "non blind watermarking of network flows"

Connected through hyperedge HE-2 (induced by source paper "tempest temporal dynamics in anonymity systems"):
  with members:
    - "onions in the crosshairs when the man really is out to get you"
    - "raptor routing attacks on privacy in tor"
    - "measuring and mitigating as level adversaries against tor"
    - "k fingerprinting a robust scalable website fingerprinting technique"
    - "hornet high speed onion routing at the network layer"
    - "avoiding the man on the wire improving tor s security with trust aware path selection"
    - "dovetail stronger anonymity in next generation internet routing"
    - "stealthy traffic analysis of low latency anonymous communication using throughput fingerprinting"

Connected through hyperedge HE-3 (induced by source paper "forming guard sets using as relationships"):
  with members:
    - "raptor routing attacks on privacy in tor"
    - "avoiding the man on the wire improving tor s security with trust aware path selection"

Connected through hyperedge HE-4 (induced by source paper "counter raptor safeguarding tor against active routing attacks"):
  with members:
    - "raptor routing attacks on privacy in tor"
    - "measuring and mitigating as level adversaries against tor"

Connected through hyperedge HE-5 (induced by source paper "holding all the ases identifying and circumventing the pitfalls of as aware tor client design"):
  with members:
    - "raptor routing attacks on privacy in tor"
    - "measuring and mitigating as level adversaries against tor"

Connected through hyperedge HE-6 (induced by source paper "the effect of dns on tor s anonymity"):
  with members:
    - "toward an efficient website fingerprinting defense"

Connected through hyperedge HE-7 (induced by source paper "avoiding the man on the wire improving tor s security with trust aware path selection"):
  with members:
    - "measuring and mitigating as level adversaries against tor"
    - "pisces anonymous communication using social networks"
    - "stealthy traffic analysis of low latency anonymous communication using throughput fingerprinting"

Connected through hyperedge HE-8 (induced by source paper "measuring and mitigating as level adversaries against tor"):
  with members:
    - "raptor routing attacks on privacy in tor"
    - "effectiveness and detection of denial of service attacks in tor"

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)