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 i motivation and construction"
  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 first of two conference papers describing the derivation of these algorithms, connection with the related literature, extensions of the original framework, and new empirical evidence. #R##N#In particular, the present paper outlines the derivation of AMP from standard sum-product belief propagation, and its extension in several directions. We also discuss relations with formal calculations based on statistical mechanics methods.

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 "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-3 (induced by source paper "expectation propagation line spectral estimation"):
  with members:
    - "a unified bayesian inference framework for generalized linear models"
    - "bilinear adaptive generalized vector approximate message passing"
    - "sparse methods for direction of arrival estimation"
    - "multi snapshot newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"
    - "an iterative receiver for ofdm with sparsity based parametric channel estimation"
    - "variational bayesian inference of line spectra"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"

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 "deep learning assisted user identification in massive machine type communications"):
  with members:
    - "on the performance gain of noma over oma in uplink communication systems"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "sparse activity detection for massive connectivity"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "on the spectral efficiency of massive mimo systems with low resolution adcs"
    - "learning optimal nonlinearities for iterative thresholding algorithms"

Connected through hyperedge HE-6 (induced by source paper "optimization of the belief propagation algorithm for distributed detection by linear data fusion techniques"):
  with members:
    - "random interruptions in cooperation for spectrum sensing in cognitive radio networks"
    - "stochastic belief propagation a low complexity alternative to the sum product algorithm"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"
    - "message passing algorithms for compressed sensing"
    - "sufficient conditions for convergence of the sum product algorithm"

Connected through hyperedge HE-7 (induced by source paper "the max product algorithm viewed as linear data fusion a distributed detection scenario"):
  with members:
    - "optimization of the belief propagation algorithm for distributed detection by linear data fusion techniques"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"
    - "message passing algorithms for compressed sensing"
    - "sufficient conditions for convergence of the sum product algorithm"

Connected through hyperedge HE-8 (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"

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)