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: "universal minimax discrete denoising under channel uncertainty"
  Abstract: The goal of a denoising algorithm is to recover a signal from its noise-corrupted observations. Perfect recovery is seldom possible and performance is measured under a given single-letter fidelity criterion. For discrete signals corrupted by a known discrete memoryless channel, the DUDE was recently shown to perform this task asymptotically optimally, without knowledge of the statistical properties of the source. In the present work we address the scenario where, in addition to the lack of knowledge of the source statistics, there is also uncertainty in the channel characteristics. We propose a family of discrete denoisers and establish their asymptotic optimality under a minimax performance criterion which we argue is appropriate for this setting. As we show elsewhere, the proposed schemes can also be implemented computationally efficiently.

Connected through hyperedge HE-1 (induced by source paper "iterative channel estimation for discrete denoising under channel uncertainty"):
  with members:
    - "learning power spectrum maps from quantized power measurements"
    - "adam a method for stochastic optimization"
    - "algorithms for discrete denoising under channel uncertainty"

Connected through hyperedge HE-2 (induced by source paper "information theoretic principles of universal discrete denoising"):
  with members:
    - "the rate distortion risk in estimation from compressed data"
    - "universal compressed sensing"
    - "deducing truth from correlation"
    - "tensor decompositions for learning latent variable models"
    - "algorithms for discrete denoising under channel uncertainty"

Connected through hyperedge HE-3 (induced by source paper "analysis of mismatched estimation errors using gradients of partition functions"):
  with members:
    - "optimum estimation via gradients of partition functions and information measures a statistical mechanical perspective"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "mmse of bad codes"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "on regret of parametric mismatch in minimum mean square error estimation"
    - "mutual information relative entropy and estimation in the poisson channel"
    - "message passing algorithms for compressed sensing"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "discrete denoising with shifts"):
  with members:
    - "universal denoising of discrete time continuous amplitude signals"

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)