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: "near optimal compressed sensing without priors parametric sure approximate message passing"
  Abstract: Both theoretical analysis and empirical evidence confirm that the approximate message passing (AMP) algorithm can be interpreted as recursively solving a signal denoising problem: at each AMP iteration, one observes a Gaussian noise perturbed original signal. Retrieving the signal amounts to a successive noise cancellation until the noise variance decreases to a satisfactory level. In this paper we incorporate the Stein's unbiased risk estimate (SURE) based parametric denoiser with the AMP framework and propose the novel parametric SURE-AMP algorithm. At each parametric SURE-AMP iteration, the denoiser is adaptively optimized within the parametric class by minimizing SURE, which depends purely on the noisy observation. In this manner, the parametric SURE-AMP is guaranteed with the best-in-class recovery and convergence rate. If the parameter family includes the families of the mimimum mean squared error (MMSE) estimators, we are able to achieve the Bayesian optimal AMP performance without knowing the signal prior. In the paper, we resort to the linear parameterization of the SURE based denoiser and propose three different kernel families as the base functions. Numerical simulations with the Bernoulli-Gaussian, $k$-dense and Student's-t signals demonstrate that the parametric SURE-AMP does not only achieve the state-of-the-art recovery but also runs more than 20 times faster than the EM-GM-GAMP algorithm.

Connected through hyperedge HE-1 (induced by source paper "amp inspired deep networks for sparse linear inverse problems"):
  with members:
    - "multi layer generalized linear estimation"
    - "hybrid approximate message passing"
    - "deep fully connected networks for video compressive sensing"
    - "learning and free energies for vector approximate message passing"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"

Connected through hyperedge HE-2 (induced by source paper "empirical bayes estimators for high dimensional sparse vectors"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "cluster seeking james stein estimators"
    - "parameterless optimal approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "denoising based turbo compressed sensing"):
  with members:
    - "d oamp a denoising based signal recovery algorithm for compressed sensing"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "from denoising to compressed sensing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "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 "sparse signal recovery using generalized approximate message passing with built in parameter estimation"):
  with members:
    - "from denoising to compressed sensing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "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-5 (induced by source paper "d oamp a denoising based signal recovery algorithm for compressed sensing"):
  with members:
    - "turbo compressed sensing with partial dft sensing matrix"
    - "from denoising to compressed sensing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "on the performance of turbo signal recovery with partial dft sensing matrices"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "compressive imaging via approximate message passing with image denoising"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "analysis of compressed sensing with spatially coupled orthogonal matrices"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "on sparse vector recovery performance in structurally orthogonal matrices via lasso"
    - "expectation maximization gaussian mixture approximate message passing"

Connected through hyperedge HE-7 (induced by source paper "bayesian optimal approximate message passing to recover structured sparse signals"):
  with members:
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "expectation maximization gaussian mixture approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for 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)