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: "generalized approximate message passing for cosparse analysis compressive sensing"
  Abstract: In cosparse analysis compressive sensing (CS), one seeks to estimate a non-sparse signal vector from noisy sub-Nyquist linear measurements by exploiting the knowledge that a given linear transform of the signal is cosparse, i.e., has sufficiently many zeros. We propose a novel approach to cosparse analysis CS based on the generalized approximate message passing (GAMP) algorithm. Unlike other AMP-based approaches to this problem, ours works with a wide range of analysis operators and regularizers. In addition, we propose a novel $\ell_0$-like soft-thresholder based on MMSE denoising for a spike-and-slab distribution with an infinite-variance slab. Numerical demonstrations on synthetic and practical datasets demonstrate advantages over existing AMP-based, greedy, and reweighted-$\ell_1$ approaches.

Connected through hyperedge HE-1 (induced by source paper "beamspace channel estimation in mmwave systems via cosparse image reconstruction technique"):
  with members:
    - "beamspace channel estimation for millimeter wave massive mimo systems with lens antenna array"
    - "multi user millimeter wave mimo with full dimensional lens antenna array"
    - "scampi a robust approximate message passing framework for compressive imaging"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-2 (induced by source paper "vector approximate message passing for the generalized linear model"):
  with members:
    - "hybrid approximate message passing"
    - "expectation consistent approximate inference generalizations and convergence"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"
    - "denoising based vector approximate message passing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "s amp approximate message passing for general matrix ensembles"

Connected through hyperedge HE-3 (induced by source paper "approximate message passing with restricted boltzmann machine priors"):
  with members:
    - "hybrid approximate message passing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "hybrid approximate message passing"
    - "hybrid approximate message passing with applications to structured sparsity"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "compressive phase retrieval via generalized approximate message passing"):
  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"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "variational free energies for compressed sensing"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "gespar efficient phase retrieval of sparse signals"
    - "phase retrieval from coded diffraction patterns"

Connected through hyperedge HE-5 (induced by source paper "iteratively reweighted ell_1 approaches to sparse composite regularization"):
  with members:
    - "sparse modeling for image and vision processing"
    - "sparsity averaging for compressive imaging"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"

Connected through hyperedge HE-6 (induced by source paper "from denoising to compressed sensing"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "does ell_p minimization outperform ell_1 minimization"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "bilinear generalized approximate message passing part i derivation"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "parameterless optimal approximate message passing"
    - "from compression to compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "bernoulli gaussian approximate message passing algorithm for compressed sensing with 1d finite difference sparsity"):
  with members:
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "from denoising to compressed sensing"
    - "parameterless optimal approximate message passing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

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)