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: "bayesian estimation of a gaussian source in middleton s class a impulsive noise"
  Abstract: This letter derives the minimum mean square error (MMSE) Bayesian estimator for a Gaussian source impaired by additive Middleton's Class-A impulsive noise. Additionally, as low-complex alternatives, the letter considers two popular suboptimal estimators, such as the soft-limiter and the blanker. The optimum MMSE thresholds for these suboptimal estimators are obtained by iteratively solving fixed point equations. The theoretical findings are corroborated by simulation results, which highlight the MSE performance penalty of the suboptimal estimators may be negligible with respect to the optimal Bayesian estimator (OBE). Noteworthy, the proposed estimators can be extended to any noise, or observation error, that can be modeled as a Gaussian-mixture.

Connected through hyperedge HE-1 (induced by source paper "on the equivalence of maximum snr and mmse estimation applications to additive non gaussian channels and quantized observations"):
  with members:
    - "on conditions for linearity of optimal estimation"
    - "pointwise relations between information and estimation in gaussian noise"
    - "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-2 (induced by source paper "non linear transformations of gaussians and gaussian mixtures with implications on estimation and information theory"):
  with members:
    - "mutual information and minimum mean square error in gaussian channels"

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)