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: "s amp for non linear observation models"
  Abstract: Recently we extended Approximate message passing (AMP) algorithm to be able to handle general invariant matrix ensembles. In this contribution we extend our S-AMP approach to non-linear observation models. We obtain generalized AMP (GAMP) algorithm as the special case when the measurement matrix has zero-mean iid Gaussian entries. Our derivation is based upon 1) deriving expectation propagation (EP) like algorithms from the stationary-points equations of the Gibbs free energy under first- and second-moment constraints and 2) applying additive free convolution in free probability theory to get low-complexity updates for the second moment quantities.

Connected through hyperedge HE-1 (induced by source paper "gaussian message passing for overloaded massive mimo noma"):
  with members:
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "mu mimo mac protocols for wireless local area networks a survey"
    - "capacity achieving iterative lmmse detection for mimo noma systems"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy efficient sensing and communication of parallel gaussian sources"

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 approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "applications of large random matrices in communications engineering"

Connected through hyperedge HE-3 (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"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "scalable inference for neuronal connectivity from calcium imaging"
    - "s amp approximate message passing for general matrix ensembles"

Connected through hyperedge HE-4 (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 approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "support recovery with sparsely sampled free random matrices"
    - "subsampling at information theoretically optimal rates"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-5 (induced by source paper "self averaging expectation propagation"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "s amp approximate message passing for general matrix ensembles"
    - "applications of large random matrices in communications engineering"
    - "expectation propagation for approximate bayesian inference"
    - "support recovery with sparsely sampled free random matrices"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"
    - "inference from correlated patterns a unified theory for perceptron learning and linear vector 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)