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: "approximate message passing algorithm with universal denoising and gaussian mixture learning"
  Abstract: We study compressed sensing (CS) signal reconstruction problems where an input signal is measured via matrix multiplication under additive white Gaussian noise. Our signals are assumed to be stationary and ergodic, but the input statistics are unknown; the goal is to provide reconstruction algorithms that are universal to the input statistics. We present a novel algorithmic framework that combines: 1) the approximate message passing CS reconstruction framework, which solves the matrix channel recovery problem by iterative scalar channel denoising; 2) a universal denoising scheme based on context quantization, which partitions the stationary ergodic signal denoising into independent and identically distributed (i.i.d.) subsequence denoising; and 3) a density estimation approach that approximates the probability distribution of an i.i.d. sequence by fitting a Gaussian mixture (GM) model. In addition to the algorithmic framework, we provide three contributions: 1) numerical results showing that state evolution holds for nonseparable Bayesian sliding-window denoisers; 2) an i.i.d. denoiser based on a modified GM learning algorithm; and 3) a universal denoiser that does not need information about the range where the input takes values from or require the input signal to be bounded. We provide two implementations of our universal CS recovery algorithm with one being faster and the other being more accurate. The two implementations compare favorably with existing universal reconstruction algorithms in terms of both reconstruction quality and runtime.

Connected through hyperedge HE-1 (induced by source paper "analysis of approximate message passing with non separable denoisers and markov random field priors"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "state evolution for approximate message passing with non separable functions"
    - "compressive hyperspectral imaging via approximate message passing"
    - "compressive imaging via approximate message passing with image denoising"
    - "from denoising to compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-2 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing"):
  with members:
    - "empirical bayes and full bayes for signal estimation"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "sparse volterra and polynomial regression models recoverability and estimation"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-3 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "phase transitions in sparse pca"
    - "compressive imaging via approximate message passing with image denoising"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "mismatched estimation in large linear systems"
    - "two part reconstruction with noisy sudocodes"

Connected through hyperedge HE-4 (induced by source paper "an overview of multi processor approximate message passing"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "multiprocessor approximate message passing with column wise partitioning"
    - "hybrid approximate message passing"
    - "statistical physics and information theory perspectives on linear inverse problems"
    - "optimal trade offs in multi processor approximate message passing"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"

Connected through hyperedge HE-5 (induced by source paper "state evolution for approximate message passing with non separable functions"):
  with members:
    - "near optimal matrix recovery from random linear measurements"
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "hybrid approximate message passing"
    - "dynamical functional theory for compressed sensing"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "approximate message passing with restricted boltzmann machine priors"
    - "compressive phase retrieval via generalized approximate message passing"
    - "sharp time data tradeoffs for linear inverse problems"

Connected through hyperedge HE-6 (induced by source paper "analysis of approximate message passing with a class of non separable denoisers"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "from denoising to compressed sensing"
    - "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-7 (induced by source paper "optimal trade offs in multi processor approximate message passing"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "hybrid approximate message passing"
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "mismatched estimation in large linear systems"
    - "signal estimation with additive error metrics in compressed sensing"
    - "distributed compressed sensing for static and time varying networks"
    - "performance regions in compressed sensing from noisy measurements"

Connected through hyperedge HE-8 (induced by source paper "plug and play admm for image restoration fixed point convergence and applications"):
  with members:
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "algorithm induced prior for image restoration"
    - "postprocessing of compressed images via sequential denoising"
    - "poisson inverse problems by the plug and play scheme"
    - "compressive imaging via approximate message passing with image denoising"
    - "group based sparse representation for image restoration"
    - "from denoising to compressed sensing"
    - "depth reconstruction from sparse samples representation algorithm and sampling"

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)