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: "the projected gsure for automatic parameter tuning in iterative shrinkage methods"
  Abstract: Linear inverse problems are very common in signal and image processing. Many algorithms that aim at solving such problems include unknown parameters that need tuning. In this work we focus on optimally selecting such parameters in iterative shrinkage methods for image deblurring and image zooming. Our work uses the projected Generalized Stein Unbiased Risk Estimator (GSURE) for determining the threshold value lambda and the iterations number K in these algorithms. The proposed parameter selection is shown to handle any degradation operator, including ill-posed and even rectangular ones. This is achieved by using GSURE on the projected expected error. We further propose an efficient greedy parameter setting scheme, that tunes the parameter while iterating without impairing the resulting deblurring performance. Finally, we provide extensive comparisons to conventional methods for parameter selection, showing the superiority of the use of the projected GSURE.

Connected through hyperedge HE-1 (induced by source paper "image restoration by iterative denoising and backward projections"):
  with members:
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "learning deep cnn denoiser prior for image restoration"
    - "image restoration using autoencoding priors"
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "the little engine that could regularization by denoising red"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "raisr rapid and accurate image super resolution"

Connected through hyperedge HE-2 (induced by source paper "hierarchical sparse and collaborative low rank representation for emotion recognition"):
  with members:
    - "structured dictionary learning for classification"
    - "collaborative multi sensor classification via sparsity based representation"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "an empirical bayes approach to recovering linearly constrained non negative sparse signals"):
  with members:
    - "bilinear generalized approximate message passing part i derivation"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "expectation maximization gaussian mixture approximate message passing"
    - "sparse projections onto the simplex"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "sparsity based poisson denoising with dictionary learning"):
  with members:
    - "image processing using smooth ordering of its patches"
    - "poisson noise reduction with non local pca"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

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.CV(Computer Vision and Pattern Recognition)