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: "onions in the crosshairs when the man really is out to get you"
  Abstract: We introduce and investigate *targeting adversaries* who selectively attack users of Tor or other secure-communication networks. We argue that attacks by such adversaries are more realistic and more significant threats to those most relying on Tor's protection than are attacks in prior analyses of Tor security. Previous research and Tor design decisions have focused on protecting against adversaries who are equally interested in any user of the network. Our adversaries selectively target users---e.g., those who visit a particular website or chat on a particular private channel---and essentially disregard Tor users other than these. We present a model of such adversaries and investigate three example cases where particular users might be targeted: a cabal conducting meetings using MTor, a published Tor multicast protocol; a cabal meeting on a private IRC channel; and users visiting a particular .onion website. In general for our adversaries, compromise is much faster and provides more feedback and possibilities for adaptation than do attacks examined in prior work. We also discuss selection of websites for targeting of their users based on the distribution across users of site activity. We describe adversaries both attempting to learn the size of a cabal meeting online or of a set of sufficiently active visitors to a targeted site and attempting to identify guards of each targeted user. We compare the threat of targeting adversaries versus previously considered adversaries, and we briefly sketch possible countermeasures for resisting targeting adversaries.

Connected through hyperedge HE-1 (induced by source paper "tempest temporal dynamics in anonymity systems"):
  with members:
    - "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"
    - "defending tor from network adversaries a case study of network path prediction"
    - "stealthy traffic analysis of low latency anonymous communication using throughput fingerprinting"

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)