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: "signal recovery using expectation consistent approximation for linear observations"
  Abstract: A signal recovery scheme is developed for linear observation systems based on expectation consistent (EC) mean field approximation. Approximate message passing (AMP) is known to be consistent with the results obtained using the replica theory, which is supposed to be exact in the large system limit, when each entry of the observation matrix is independently generated from an identical distribution. However, this is not necessarily the case for general matrices. We show that EC recovery exhibits consistency with the replica theory for a wider class of random observation matrices. This is numerically confirmed by experiments for the Bayesian optimal signal recovery of compressed sensing using random row-orthogonal matrices.

Connected through hyperedge HE-1 (induced by source paper "macroscopic analysis of vector approximate message passing in a model mismatch setting"):
  with members:
    - "vector approximate message passing for the generalized linear model"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "inference from correlated patterns a unified theory for perceptron learning and linear vector channels"

Connected through hyperedge HE-2 (induced by source paper "expectation propagation for approximate inference free probability framework"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "dynamical functional theory for compressed sensing"
    - "expectation consistent approximate inference generalizations and convergence"
    - "self averaging expectation propagation"
    - "vector approximate message passing for the generalized linear model"
    - "s amp for non linear observation models"
    - "s amp approximate message passing for general matrix ensembles"
    - "applications of large random matrices in communications engineering"

Connected through hyperedge HE-3 (induced by source paper "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"):
  with members:
    - "the mutual information in random linear estimation beyond i i d matrices"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "the mutual information in random linear estimation"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "performance improvement of iterative multiuser detection for large sparsely spread cdma systems by spatial coupling"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "s amp approximate message passing for general matrix ensembles"

Connected through hyperedge HE-4 (induced by source paper "multi layer generalized linear estimation"):
  with members:
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "s amp approximate message passing for general matrix ensembles"
    - "support recovery with sparsely sampled free random matrices"
    - "optimal phase transitions in compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "dynamical functional theory for compressed sensing"):
  with members:
    - "self averaging expectation propagation"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "s amp approximate message passing for general matrix ensembles"
    - "support recovery with sparsely sampled free random matrices"
    - "generating functional analysis for iterative cdma multiuser detectors"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"):
  with members:
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "s amp approximate message passing for general matrix ensembles"
    - "on sparse vector recovery performance in structurally orthogonal matrices via lasso"
    - "asymptotic mmse analysis under sparse representation modeling"
    - "statistical mechanics approach to sparse noise denoising"
    - "support recovery with sparsely sampled free random matrices"

Connected through hyperedge HE-7 (induced by source paper "expectation consistent approximate inference generalizations and convergence"):
  with members:
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "s amp for non linear observation models"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "scalable inference for neuronal connectivity from calcium imaging"

Connected through hyperedge HE-8 (induced by source paper "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"):
  with members:
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "s amp for non linear observation models"
    - "s amp approximate message passing for general matrix ensembles"
    - "support recovery with sparsely sampled free random matrices"
    - "subsampling at information theoretically optimal rates"
    - "generalized approximate message passing for estimation with random linear mixing"

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)