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: "adaptive image denoising by mixture adaptation"
  Abstract: We propose an adaptive learning procedure to learn patch-based image priors for image denoising. The new algorithm, called the expectation-maximization (EM) adaptation, takes a generic prior learned from a generic external database and adapts it to the noisy image to generate a specific prior. Different from existing methods that combine internal and external statistics in  ad hoc  ways, the proposed algorithm is rigorously derived from a Bayesian hyper-prior perspective. There are two contributions of this paper. First, we provide full derivation of the EM adaptation algorithm and demonstrate methods to improve the computational complexity. Second, in the absence of the latent clean image, we show how EM adaptation can be modified based on pre-filtering. The experimental results show that the proposed adaptation algorithm yields consistently better denoising results than the one without adaptation and is superior to several state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "from rank estimation to rank approximation rank residual constraint for image denoising"):
  with members:
    - "the perception distortion tradeoff"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "image denoising via group sparsity residual constraint"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "group sparsity residual with non local samples for image denoising"):
  with members:
    - "image denoising using group sparsity residual and external nonlocal self similarity prior"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "group based sparse representation for image restoration"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-3 (induced by source paper "optimal combination of image denoisers"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"

Connected through hyperedge HE-4 (induced by source paper "group sparsity residual constraint for image denoising"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "non convex weighted lp minimization based group sparse representation framework for image denoising"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "adaptive image denoising by targeted databases"
    - "frist flipping and rotation invariant sparsifying transform learning and applications"
    - "boosting of image denoising algorithms"
    - "group based sparse representation for image restoration"
    - "image restoration using joint statistical modeling in a space transform domain"

Connected through hyperedge HE-5 (induced by source paper "image denoising using group sparsity residual and external nonlocal self similarity prior"):
  with members:
    - "group sparsity residual constraint for image denoising"
    - "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"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "image denoising via group sparsity residual constraint"
    - "group based sparse representation for image restoration"
    - "image restoration using joint statistical modeling in a space transform domain"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-6 (induced by source paper "accelerating gmm based patch priors for image restoration three ingredients for a 100 times speed up"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "example based image synthesis via randomized patch matching"
    - "accurate image super resolution using very deep convolutional networks"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "insights into analysis operator learning from patch based sparse models to higher order mrfs"
    - "image super resolution using deep convolutional networks"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-7 (induced by source paper "understanding symmetric smoothing filters a gaussian mixture model perspective"):
  with members:
    - "a gaussian mixture mrf for model based iterative reconstruction with applications to low dose x ray ct"
    - "adaptive image denoising by targeted databases"
    - "monte carlo non local means random sampling for large scale image filtering"
    - "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)