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 "dynamic traitor tracing for arbitrary alphabets divide and conquer"):
  Abstract: We give a generic divide-and-conquer approach for constructing collusion-resistant probabilistic dynamic traitor tracing schemes with larger alphabets from schemes with smaller alphabets. This construction offers a linear tradeoff between the alphabet size and the codelength. In particular, we show that applying our results to the binary dynamic Tardos scheme of Laarhoven et al. leads to schemes that are shorter by a factor equal to half the alphabet size. Asymptotically, these codelengths correspond, up to a constant factor, to the fingerprinting capacity for static probabilistic schemes. This gives a hierarchy of probabilistic dynamic traitor tracing schemes, and bridges the gap between the low bandwidth, high codelength scheme of Laarhoven et al. and the high bandwidth, low codelength scheme of Fiat and Tassa.

Includes members:
  Member "optimal symmetric tardos traitor tracing schemes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nearest neighbor decoding for tardos fingerprinting codes")
  Member "dynamic tardos traitor tracing schemes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nearest neighbor decoding for tardos fingerprinting codes")
  Member "on the saddle point solution and the large coalition asymptotics of fingerprinting games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "asymptotics of fingerprinting and group testing capacity achieving log likelihood decoders")
  Member "asymptotic fingerprinting capacity for non binary alphabets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "asymptotics of fingerprinting and group testing capacity achieving log likelihood decoders")
  Member "tardos fingerprinting is better than we thought":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nearest neighbor decoding for tardos fingerprinting codes")

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)