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: "message passing algorithms for compressed sensing ii analysis and validation"
  Abstract: In a recent paper, the authors proposed a new class of low-complexity iterative thresholding algorithms for reconstructing sparse signals from a small set of linear measurements \cite{DMM}. The new algorithms are broadly referred to as AMP, for approximate message passing. This is the second of two conference papers describing the derivation of these algorithms, connection with related literature, extensions of original framework, and new empirical evidence. #R##N#This paper describes the state evolution formalism for analyzing these algorithms, and some of the conclusions that can be drawn from this formalism. We carried out extensive numerical simulations to confirm these predictions. We present here a few representative results.

Connected through hyperedge HE-1 (induced by source paper "random access for massive machine type communications"):
  with members:
    - "beamwidth control for noma in hybrid mmwave communication systems"
    - "design of non orthogonal beamspace multiple access for cellular internet of things"
    - "on the performance gain of noma over oma in uplink single cell systems"
    - "multi beam noma for hybrid mmwave systems"
    - "joint pilot and payload power control for uplink mimo noma with mrc sic receivers"
    - "reliability based windowed decoding for spatially coupled ldpc codes"
    - "short packet downlink transmission with non orthogonal multiple access"
    - "a multi beam noma framework for hybrid mmwave systems"

Connected through hyperedge HE-2 (induced by source paper "optimal data detection in large mimo"):
  with members:
    - "on the performance of mismatched data detection in large mimo systems"
    - "precise error analysis of regularized m estimators in high dimensions"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "compressive phase retrieval via generalized approximate message passing"
    - "does ell_p minimization outperform ell_1 minimization"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"

Connected through hyperedge HE-3 (induced by source paper "compressive massive random access for massive machine type communications mmtc"):
  with members:
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "compressive sensing techniques for next generation wireless communications"
    - "approximate message passing with nearest neighbor sparsity pattern learning"
    - "grant free rateless multiple access a novel massive access scheme for internet of things"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-4 (induced by source paper "performance analysis of approximate message passing for distributed compressed sensing"):
  with members:
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "performance limits for noisy multimeasurement vector problems"
    - "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"
    - "omp based joint sparsity pattern recovery under communication constraints"
    - "efficient high dimensional inference in the multiple measurement vector problem"

Connected through hyperedge HE-5 (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-6 (induced by source paper "inferring sparsity compressed sensing using generalized restricted boltzmann machines"):
  with members:
    - "hybrid approximate message passing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "approximate message passing with restricted boltzmann machine priors"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "a survey of sparse representation algorithms and applications"
    - "a deep learning approach to structured signal recovery"
    - "hybrid approximate message passing"
    - "hybrid approximate message passing with applications to structured sparsity"

Connected through hyperedge HE-7 (induced by source paper "vlsi friendly framework for scalable video coding based on compressed sensing"):
  with members:
    - "a new compressive video sensing framework for mobile broadcast"
    - "message passing algorithms for compressed sensing"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "inference for generalized linear models via alternating directions and bethe free energy minimization"):
  with members:
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "bilinear generalized approximate message passing part i derivation"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "variational free energies for compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "on convergence of approximate message passing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"

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)