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 "holding all the ases identifying and circumventing the pitfalls of as aware tor client design"):
  Abstract: Traffic correlation attacks to de-anonymize Tor users are possible when an adversary is in a position to observe traffic entering and exiting the Tor network. Recent work has brought attention to the threat of these attacks by network-level adversaries (e.g., Autonomous Systems). We perform a historical analysis to understand how the threat from AS-level traffic correlation attacks has evolved over the past five years. We find that despite a large number of new relays added to the Tor network, the threat has grown. This points to the importance of increasing AS-level diversity in addition to capacity of the Tor network. #R##N#We identify and elaborate on common pitfalls of AS-aware Tor client design and construction. We find that succumbing to these pitfalls can negatively impact three major aspects of an AS-aware Tor client -- (1) security against AS-level adversaries, (2) security against relay-level adversaries, and (3) performance. Finally, we propose and evaluate a Tor client -- Cipollino -- which avoids these pitfalls using state-of-the-art in network-measurement. Our evaluation shows that Cipollino is able to achieve better security against network-level adversaries while maintaining security against relay-level adversaries and

Includes members:
  Member "raptor routing attacks on privacy in tor":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blind adversarial network perturbations")
  Member "measuring and mitigating as level adversaries against tor":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "deepcorr strong flow correlation attacks on tor using deep learning")

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)