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: "boosting of image denoising algorithms"
  Abstract: In this paper we propose a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following "SOS" procedure: (i) (S)trengthen the signal by adding the previous denoised image to the degraded input image, (ii) (O)perate the denoising method on the strengthened image, and (iii) (S)ubtract the previous denoised image from the restored signal-strengthened outcome. The convergence of this process is studied for the K-SVD image denoising and related algorithms. Still in the context of K-SVD image denoising, we introduce an interesting interpretation of the SOS algorithm as a technique for closing the gap between the local patch-modeling and the global restoration task, thereby leading to improved performance. In a quest for the theoretical origin of the SOS algorithm, we provide a graph-based interpretation of our method, where the SOS recursive update effectively minimizes a penalty function that aims to denoise the image, while being regularized by the graph Laplacian. We demonstrate the SOS boosting algorithm for several leading denoising methods (K-SVD, NLM, BM3D, and EPLL), showing tendency to further improve denoising performance.

Connected through hyperedge HE-1 (induced by source paper "deep k svd denoising"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "non local recurrent network for image restoration"
    - "deep image prior"
    - "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"
    - "benchmarking denoising algorithms with real photographs"
    - "universal denoising networks a novel cnn architecture for image denoising"
    - "the little engine that could regularization by denoising red"

Connected through hyperedge HE-2 (induced by source paper "class aware fully convolutional gaussian and poisson denoising"):
  with members:
    - "class specific image denoising using importance sampling"
    - "image restoration by iterative denoising and backward projections"
    - "deep burst denoising"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "blind image deblurring using class adapted image priors"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"

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 "rendition reclaiming what a black box takes away"):
  with members:
    - "lipschitz properties for deep convolutional networks"
    - "the little engine that could regularization by denoising red"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-5 (induced by source paper "multimodal image denoising based on coupled dictionary learning"):
  with members:
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"
    - "hyperspectral pansharpening a review"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-6 (induced by source paper "deep class aware denoising"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "postprocessing of compressed images via sequential denoising"
    - "deep gaussian conditional random field network a model based deep network for discriminative denoising"
    - "poisson inverse problems by the plug and play scheme"

Connected through hyperedge HE-7 (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"
    - "adaptive image denoising by mixture adaptation"
    - "plug and play admm for image restoration fixed point convergence and applications"

Connected through hyperedge HE-8 (induced by source paper "on the global local dichotomy in sparsity modeling"):
  with members:
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "local sparsity and recovery of fusion frames structured signals"
    - "working locally thinking globally part ii stability and algorithms for convolutional sparse coding"
    - "approximation vector machines for large scale online learning"
    - "structured sparsity discrete and convex approaches"
    - "sparse modeling for image and vision processing"
    - "uniform recovery of fusion frame structured sparse signals"
    - "shift invariance sparse coding for audio classification"

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)