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: "adaptive damping and mean removal for the generalized approximate message passing algorithm"
  Abstract: The generalized approximate message passing (GAMP) algorithm is an efficient method of MAP or approximate-MMSE estimation of x observed from a noisy version of the transform coefficients z = Ax. In fact, for large zero-mean i.i.d sub-Gaussian A, GAMP is characterized by a state evolution whose fixed points, when unique, are optimal. For generic A, however, GAMP may diverge. In this paper, we propose adaptive-damping and mean-removal strategies that aim to prevent divergence. Numerical results demonstrate significantly enhanced robustness to non-zero-mean, rank-deficient, column-correlated, and ill-conditioned A.

Connected through hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "near optimal matrix recovery from random linear measurements"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-2 (induced by source paper "generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior"):
  with members:
    - "sparse activity detection for massive connectivity"
    - "a lse and sparse message passing based channel estimation for mmwave mimo systems"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "low rank tensor decomposition aided channel estimation for millimeter wave mimo ofdm systems"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"

Connected through hyperedge HE-3 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo"):
  with members:
    - "gaussian message passing for overloaded massive mimo noma"
    - "feedforward architectures for decentralized precoding in massive mu mimo systems"
    - "efficient dsp and circuit architectures for massive mimo state of the art and future directions"
    - "decentralized baseband processing for massive mu mimo systems"
    - "optimally tuned nonparametric linear equalization for massive mu mimo systems"
    - "on the achievable rates of decentralized equalization in massive mu mimo systems"
    - "on the performance of mismatched data detection in large mimo systems"
    - "performance trade offs in multi processor approximate message passing"

Connected through hyperedge HE-4 (induced by source paper "trainable ista for sparse signal recovery"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "learning to decode linear codes using deep learning"
    - "a survey of sparse representation algorithms and applications"

Connected through hyperedge HE-5 (induced by source paper "sparse bayesian learning using approximate message passing with unitary transformation"):
  with members:
    - "a gamp based low complexity sparse bayesian learning algorithm"
    - "approximate message passing with unitary transformation"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-6 (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"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"
    - "s amp for non linear observation models"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy efficient sensing and communication of parallel gaussian sources"

Connected through hyperedge HE-7 (induced by source paper "plug in estimation in high dimensional linear inverse problems a rigorous analysis"):
  with members:
    - "hybrid approximate message passing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "state evolution for approximate message passing with non separable functions"
    - "expectation consistent approximate inference generalizations and convergence"
    - "parametric bilinear generalized approximate message passing"
    - "self calibration and biconvex compressive sensing"
    - "maximin analysis of message passing algorithms for recovering block sparse signals"
    - "hybrid approximate message passing"

Connected through hyperedge HE-8 (induced by source paper "approximate message passing for underdetermined audio source separation"):
  with members:
    - "s amp approximate message passing for general matrix ensembles"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

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)