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: "on optimum parameter modulation estimation from a large deviations perspective"
  Abstract: We consider the problem of jointly optimum modulation and estimation of a real-valued random parameter, conveyed over an additive white Gaussian noise (AWGN) channel, where the performance metric is the large deviations behavior of the estimator, namely the exponential decay rate (as a function of the observation time) of the probability that the estimation error would exceed a certain threshold. Our basic result is in providing an exact characterization of the fastest achievable exponential decay rate, among all possible modulator-estimator (transmitter-receiver) pairs, where the modulator is limited only in the signal power, but not in bandwidth. This exponential rate turns out to be given by the reliability function of the AWGN channel. We also discuss several ways to achieve this optimum performance, and one of them is based on quantization of the parameter, followed by optimum channel coding and modulation, which gives rise to a separation-based transmitter, if one views this setting from the perspective of joint source-channel coding. This is in spite of the fact that, in general, when error exponents are considered, the source-channel separation theorem does not hold true. We also discuss several observations, modifications, and extensions of this result in several directions, including other channels, and the case of multidimensional parameter vectors. One of our findings concerning the latter is that there is an abrupt threshold effect in the dimensionality of the parameter vector: below a certain critical dimension, the probability of excess estimation error may still decay exponentially, but beyond this value, it must converge to unity.

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"
    - "exponential error bounds on parameter modulation estimation for discrete memoryless channels"

Connected through hyperedge HE-2 (induced by source paper "lower bounds on parameter modulation estimation under bandwidth constraints"):
  with members:
    - "exponential error bounds on parameter modulation estimation for discrete memoryless channels"
    - "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-3 (induced by source paper "converse bounds on modulation estimation performance for the gaussian multiple access channel"):
  with members:
    - "exponential error bounds on parameter modulation estimation for discrete memoryless channels"

Connected through hyperedge HE-4 (induced by source paper "exponential error bounds on parameter modulation estimation for discrete memoryless channels"):
  with members:
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"
    - "data processing inequalities based on a certain structured class of information measures with application to estimation theory"
    - "a lower bound on the bayesian mse based on the optimal bias function"

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)