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: "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
  Abstract: We propose two novel approaches for the recovery of an (approximately) sparse signal from noisy linear measurements in the case that the signal is a priori known to be non-negative and obey given linear equality constraints, such as a simplex signal. This problem arises in, e.g., hyperspectral imaging, portfolio optimization, density estimation, and certain cases of compressive imaging. Our first approach solves a linearly constrained non-negative version of LASSO using the max-sum version of the generalized approximate message passing (GAMP) algorithm, where we consider both quadratic and absolute loss, and where we propose a novel approach to tuning the LASSO regularization parameter via the expectation maximization (EM) algorithm. Our second approach is based on the sum-product version of the GAMP algorithm, where we propose the use of a Bernoulli non-negative Gaussian-mixture signal prior and a Laplacian likelihood and propose an EM-based approach to learning the underlying statistical parameters. In both approaches, the linear equality constraints are enforced by augmenting GAMP's generalized-linear observation model with noiseless pseudo-measurements. Extensive numerical experiments demonstrate the state-of-the-art performance of our proposed approaches.

Connected through hyperedge HE-1 (induced by source paper "learning and free energies for vector approximate message passing"):
  with members:
    - "hybrid approximate message passing"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "the mutual information in random linear estimation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a survey of stochastic simulation and optimization methods in signal processing"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "communication efficient distributed dual coordinate ascent"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"

Connected through hyperedge HE-3 (induced by source paper "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"):
  with members:
    - "stable low rank matrix recovery via null space properties"
    - "on the gap between rip properties and sparse recovery conditions"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "low rank matrix recovery from rank one measurements"
    - "ripless compressed sensing from anisotropic measurements"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-4 (induced by source paper "rectified gaussian scale mixtures and the sparse non negative least squares problem"):
  with members:
    - "a gamp based low complexity sparse bayesian learning algorithm"
    - "type i and type ii bayesian methods for sparse signal recovery using scale mixtures"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "spatiotemporal sparse bayesian learning with applications to compressed sensing of multichannel physiological signals"
    - "robust face recognition via block sparse bayesian learning"
    - "expectation maximization gaussian mixture approximate message passing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-5 (induced by source paper "two dimensional pattern coupled sparse bayesian learning via generalized approximate message passing"):
  with members:
    - "pattern coupled sparse bayesian learning for recovery of block sparse signals"
    - "expectation maximization gaussian mixture approximate message passing"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-6 (induced by source paper "type i and type ii bayesian methods for sparse signal recovery using scale mixtures"):
  with members:
    - "expectation maximization gaussian mixture approximate message passing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-7 (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"
    - "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"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"

Connected through hyperedge HE-8 (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"
    - "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"
    - "generalized approximate message passing for cosparse analysis compressive sensing"
    - "gespar efficient phase retrieval of sparse signals"
    - "phase retrieval from coded diffraction patterns"

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)