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 "reconstruction of c c channel for structured p2p botnet"):
  Abstract: Botnets are problematic malware and a big challenge for researchers who have always been unsuccessful in preventing their attacks. Not only have P2P botnets increased the robustness of C&C channels, they have also made the detection of botmaster harder. In this paper the topologies of C&C channel of P2P botnets are reconstructed for an easier containment of P2P botnets and for helping the investigator with his guessing in the detection of command issuing sources. #R##N#In the method presented in this paper, the connections between nodes are estimated by collecting receiving times of several cascades and by having random network parameters of C&C channel. In the simulation of structured P2P botnet, by collecting the receiving times of 22 cascades in a 1000-member network with mean node degree 50, our method accurately estimates more than 90% of the edges. If the receiving times of just half of the nodes are collected, by having 95 cascades, this recall is obtained.

Includes members:
  Member "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "topology discovery of sparse random graphs with few participants":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topology discovery using path interference")
  Member "defense strategies against modern botnets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reconstruction of c c channel for p2p botnet")

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)