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 "universal fingerprinting capacity and random coding exponents"):
  Abstract: This paper studies fingerprinting (traitor tracing) games in which the number of colluders and the collusion channel are unknown. The fingerprints are embedded into host sequences representing signals to be protected and provide the receiver with the capability to trace back pirated copies to the colluders. The colluders and the fingerprint embedder are subject to signal fidelity constraints. Our problem setup unifies the signal-distortion and Boneh-Shaw formulations of fingerprinting. The fundamental tradeoffs between fingerprint codelength, number of users, number of colluders, fidelity constraints, and decoding reliability are then determined. Several bounds on fingerprinting capacity have been presented in recent literature. This paper derives exact capacity formulas and presents a new randomized fingerprinting scheme with the following properties: (1) the encoder and receiver assume a nominal coalition size but do not need to know the actual coalition size and the collusion channel; (2) a tunable parameter $\Delta$ trades off false-positive and false-negative error exponents; (3) the receiver provides a reliability metric for its decision; and (4) the scheme is capacity-achieving when the false-positive exponent $\Delta$ tends to zero and the nominal coalition size coincides with the actual coalition size. #R##N#A fundamental component of the new scheme is the use of a "time-sharing" randomized sequence. The decoder is a maximum penalized mutual information decoder, where the significance of each candidate coalition is assessed relative to a threshold, and the penalty is proportional to the coalition size. A much simpler {\em threshold decoder} that satisfies properties (1)---(3) above but not (4) is also given.

Includes members:
  Member "saddle point solution of the fingerprinting capacity game under the marking assumption":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asymptotics of fingerprinting and group testing tight bounds from channel capacities")
  Member "two level fingerprinting codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "signature codes for weighted binary adder channel and multimedia fingerprinting")
  Member "worst case attacks against binary probabilistic traitor tracing codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nearest neighbor decoding for tardos fingerprinting codes")
  Member "on the fingerprinting capacity under the marking assumption":
    also connected through other hyperedges:
      (none)
  Member "capacity and random coding exponents for channel coding with side information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the capacity of the carbon copy onto dirty paper channel")

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)