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 "asymptotics of fingerprinting and group testing capacity achieving log likelihood decoders"):
  Abstract: We study the large-coalition asymptotics of fingerprinting and group testing and derive explicit decoders that provably achieve capacity for many of the considered models. We do this both for simple decoders (which are fast but commonly require larger code lengths) and for joint decoders (which may be slower but achieve the best code lengths). We further make the distinction between informed decoding, where the pirate strategy is exactly known, and uninformed decoding, and we design decoding schemes for both settings.#R##N##R##N#For fingerprinting, we show that if the pirate strategy is known, the Neyman-Pearson-based log-likelihood decoders provably achieve capacity, regardless of the strategy. The decoder built against the interleaving attack is further shown to be a universal decoder, able to deal with arbitrary attacks and achieving the uninformed capacity. This universal decoder is shown to be closely related to the Lagrange-optimized decoder of Oosterwijk et al. and the empirical mutual information decoder of Moulin. Joint decoders are also proposed, and we conjecture that these also achieve the corresponding joint capacities.#R##N##R##N#For group testing, the simple decoder for the classical model is shown to be more efficient than the one of Chan et al. and it provably achieves the simple group testing capacity. For generalizations of this model such as noisy group testing, the resulting simple decoders also achieve the corresponding simple capacities.

Includes members:
  Member "asymptotics of fingerprinting and group testing tight bounds from channel capacities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "group testing an information theory perspective")
  Member "optimal symmetric tardos traitor tracing schemes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nearest neighbor decoding for tardos fingerprinting codes")
  Member "capacities and capacity achieving decoders for various fingerprinting games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nearest neighbor decoding for tardos fingerprinting codes")
  Member "enhanced blind decoding of tardos codes with new map based functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nearest neighbor decoding for tardos fingerprinting codes")
  Member "dynamic traitor tracing schemes revisited":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asymptotics of fingerprinting and group testing tight bounds from channel capacities")
  Member "discrete distributions in the tardos scheme revisited":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nearest neighbor decoding for tardos fingerprinting codes")
  Member "efficient probabilistic group testing based on traitor tracing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the optimality of some group testing algorithms")
  Member "dynamic tardos traitor tracing schemes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.IT(Information Theory)