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 "exponential error bounds on parameter modulation estimation for discrete memoryless channels"):
  Abstract: We consider the problem of modulation and estimation of a random parameter U to be conveyed across a discrete memoryless channel. Upper and lower bounds are derived for the best achievable exponential decay rate of a general moment of the estimation error, E|U-U|ρ, ρ ≥ 0, when both the modulator and the estimator are subjected to optimization. These exponential error bounds turn out to be intimately related to error exponents of channel coding and to channel capacity. While in general, there is some gap between the upper and the lower bounds, they asymptotically coincide both for very small and for very large values of the moment power ρ. This means that our achievability scheme, which is based on simple quantization of U followed by channel coding, is nearly optimum in both limits. Some additional properties of the bounds are discussed and demonstrated, and finally, an extension to the case of a multidimensional parameter vector is outlined, with the principal conclusion that our upper and lower bounds asymptotically coincide also for a high dimensionality.

Includes members:
  Member "universal decoding for arbitrary channels relative to a given class of decoding metrics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the bee identification error exponent with absentee bees")
  Member "on optimum parameter modulation estimation from a large deviations perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weak noise modulation estimation of vector parameters")
  Member "data processing inequalities based on a certain structured class of information measures with application to estimation theory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fundamental inequalities and identities involving mutual and directed informations in closed loop systems")
  Member "a lower bound on the bayesian mse based on the optimal bias function":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "performance of time delay estimation in a cognitive radar")

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)