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: "empirical bayes and full bayes for signal estimation"
  Abstract: We consider signals that follow a parametric dis- tribution where the parameter values are unknown. To estimate such signals from noisy measurements in scalar channels, we study the empirical performance of an empirical Bayes (EB) approach and a full Bayes (FB) approach. We then apply EB and FB to solve compressed sensing (CS) signal estimation problems by successively denoising a scalar Gaussian channel within an approximate message passing (AMP) framework. Our numerical results show that FB achieves better performance than EB in scalar channel denoising problems when the signal dimension is small. In the CS setting, the signal dimension must be large enough for AMP to work well; for large signal dimensions, AMP has similar performance with FB and EB.

Connected through hyperedge HE-1 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing"):
  with members:
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "sparse volterra and polynomial regression models recoverability and estimation"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-2 (induced by source paper "compressive imaging via approximate message passing with image denoising"):
  with members:
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "signal estimation with additive error metrics in compressed sensing"
    - "from denoising to compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "parameterless optimal approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "model based compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "compressed sensing via universal denoising and approximate message passing"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "signal estimation with additive error metrics in compressed sensing"
    - "from denoising to compressed sensing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "information complexity and estimation"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"

Connected through hyperedge HE-4 (induced by source paper "recovery from linear measurements with complexity matching universal signal estimation"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "compressed sensing via universal denoising and approximate message passing"
    - "performance regions in compressed sensing from noisy measurements"
    - "expectation maximization gaussian mixture approximate message passing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "an mcmc approach to universal lossy compression of analog sources"
    - "an mdl framework for sparse coding and dictionary learning"
    - "information complexity and estimation"

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)