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: "wtf pad toward an efficient website fingerprinting defense for tor"
  Abstract: Website Fingerprinting attacks are conducted by a low-resource local adversary who observes the (encrypted) traffic between an anonymity system and one or more of its clients. With this attack, the adversary can recover the user's otherwise anonymized web browsing activity by matching the observed traffic with prerecorded web page traffic templates. The defenses that have been proposed to counter these attacks are effective, but they are impractical for deployment in real-world systems due to their high cost in terms of both added delay to access webpages and bandwidth overhead. Further, these defenses have been designed to counter attacks that, despite their high success rates, have been criticized for assuming unrealistic attack conditions in the evaluation setting. #R##N#In this paper, we show that there are lightweight defenses that provide a sufficient level of security against website fingerprinting, particularly in realistic evaluation settings. In particular, we propose a novel defense based on Adaptive Padding, a link-padding strategy for low-latency anonymity systems that we have adapted for Tor. With this countermeasure in a closed-world setting, we observe a reduction in the accuracy of the state-of-the-art attack from 91% to 20%, while introducing zero latency overhead and less than 60% bandwidth overhead. In an open-world setting with 50 pages that the attacker is monitoring and 2,000 other websites that users visit, the attack precision is just 1% and drops further as the number of sites grows. For the implementation and evaluation of the defense, we have developed a tool for evaluating the traffic analysis resistance properties of Tor's Pluggable Transports that we hope will contribute to future research on Tor and traffic analysis.

Connected through hyperedge HE-1 (induced by source paper "k fingerprinting a robust scalable website fingerprinting technique"):
  with members:
    - "dobbs towards a comprehensive dataset to study the browsing behavior of online users"

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)