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 "collusion resistant and privacy preserving p2p multimedia distribution based on recombined fingerprinting"):
  Abstract: Efficient P2P content distribution avoiding expensive cryptographic protocols.Copyright protection and revocable privacy are guaranteed to merchant and buyer.Recombination approach providing automatically constructed fingerprints.Segment-wise construction of fingerprints using Nuida etźal.'s c-secure codes.Novel traitor-tracing mechanism in the encrypted domain. Recombined fingerprints have been suggested as a convenient approach to improve the efficiency of anonymous fingerprinting for the legal distribution of copyrighted multimedia contents in P2P systems. The recombination idea is inspired by the principles of mating, recombination and heredity of the DNA sequences of living beings, but applied to binary sequences, like in genetic algorithms. However, the existing recombination-based fingerprinting systems do not provide a convenient solution for collusion resistance, since they require "double-layer" fingerprinting codes, making the practical implementation of such systems a challenging task. In fact, collusion resistance is regarded as the most relevant requirement of a fingerprinting scheme, and the lack of any acceptable solution to this problem would possibly deter content merchants from deploying any practical implementation of the recombination approach. In this paper, this drawback is overcome by introducing two non-trivial improvements, paving the way for a future real-life application of recombination-based systems. First, Nuida etźal.'s collusion-resistant codes are used in segment-wise fashion for the first time. Second, a novel version of the traitor-tracing algorithm is proposed in the encrypted domain, also for the first time, making it possible to provide the buyers with security against framing. In addition, the proposed method avoids the use of public-key cryptography for the multimedia content and expensive cryptographic protocols, leading to excellent performance in terms of both computational and communication burdens. In fact, after bootstrapping, the merchant is no longer required to participate in any file transfer, reducing the investment in equipment required for content distribution to the bare minimum. The paper also analyzes the security and privacy properties of the proposed system both formally and informally, whereas the collusion resistance and the performance of the method are shown by means of experiments and simulations.

Includes members:
  Member "psum peer to peer multimedia content distribution using collusion resistant fingerprinting":
    also connected through other hyperedges:
      (none)
  Member "an asymmetric fingerprinting scheme based on tardos codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "psum peer to peer multimedia content distribution using collusion resistant fingerprinting")

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)