human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "weak noise modulation estimation of vector parameters"):
  with members:
    - "trade offs between weak noise estimation performance and outage exponents in nonlinear modulation"
    - "converse bounds on modulation estimation performance for the gaussian multiple access channel"
    - "on optimum parameter modulation estimation from a large deviations perspective"

Connected through hyperedge HE-2 (induced by source paper "on the minimum mean p th error in gaussian noise channels and its applications"):
  with members:
    - "on communication through a gaussian channel with an mmse disturbance constraint"
    - "lower bounds on parameter modulation estimation under bandwidth constraints"
    - "interference as noise friend or foe"
    - "on conditions for linearity of optimal estimation"
    - "mmse of bad codes"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "lower bounds on parameter modulation estimation under bandwidth constraints"):
  with members:
    - "on optimum parameter modulation estimation from a large deviations perspective"
    - "threshold effects in parameter estimation as phase transitions in statistical mechanics"
    - "unequal error protection an information theoretic perspective"
    - "error exponents for broadcast channels with degraded message sets"
    - "a lower bound on the bayesian mse based on the optimal bias function"

Connected through hyperedge HE-4 (induced by source paper "converse bounds on modulation estimation performance for the gaussian multiple access channel"):
  with members:
    - "on optimum parameter modulation estimation from a large deviations perspective"

Question: Please tell me which class the center node 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)