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: "image processing using smooth ordering of its patches"
  Abstract: We propose an image processing scheme based on reordering of its patches. For a given corrupted image, we extract all patches with overlaps, refer to these as coordinates in high-dimensional space, and order them such that they are chained in the “shortest possible path,” essentially solving the traveling salesman problem. The obtained ordering applied to the corrupted image implies a permutation of the image pixels to what should be a regular signal. This enables us to obtain good recovery of the clean image by applying relatively simple one-dimensional smoothing operations (such as filtering or interpolation) to the reordered set of pixels. We explore the use of the proposed approach to image denoising and inpainting, and show promising results in both cases.

Connected through hyperedge HE-1 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization"):
  with members:
    - "image restoration by iterative denoising and backward projections"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "learning deep cnn denoiser prior for image restoration"
    - "enhanced low rank matrix approximation"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "marlow a joint multiplanar autoregressive and low rank approach for image completion"
    - "partial sum minimization of singular values in robust pca algorithm and applications"

Connected through hyperedge HE-2 (induced by source paper "good similar patches for image denoising"):
  with members:
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "burst denoising with kernel prediction networks"

Connected through hyperedge HE-3 (induced by source paper "the power of complementary regularizers image recovery via transform learning and low rank modeling"):
  with members:
    - "non local recurrent network for image restoration"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
    - "image denoising via group sparsity residual constraint"
    - "frist flipping and rotation invariant sparsifying transform learning and applications"

Connected through hyperedge HE-4 (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-5 (induced by source paper "image super resolution via feature augmented random forest"):
  with members:
    - "joint maximum purity forest with application to image super resolution"
    - "cascaded face alignment via intimacy definition feature"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep networks for image super resolution with sparse prior"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "restoration by compression"):
  with members:
    - "an efficient algorithm for compression based compressed sensing"
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "the little engine that could regularization by denoising red"
    - "postprocessing of compressed images via sequential denoising"
    - "poisson inverse problems by the plug and play scheme"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-7 (induced by source paper "example based image synthesis via randomized patch matching"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "con patch when a patch meets its context"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "image to image translation with conditional adversarial networks"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "pixel recurrent neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "texture synthesis using convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "patch ordering as a regularization for inverse problems in image processing"):
  with members:
    - "boosting of image denoising algorithms"
    - "sparsity based poisson denoising with dictionary learning"
    - "redundant wavelets on graphs and high dimensional data clouds"
    - "poisson noise reduction with non local pca"
    - "generalized tree based wavelet transform"
    - "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)