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: "denoising prior driven deep neural network for image restoration"
  Abstract: Deep neural networks (DNNs) have shown very promising results for various image restoration (IR) tasks. However, the design of network architectures remains a major challenging for achieving further improvements. While most existing DNN-based methods solve the IR problems by directly mapping low quality images to desirable high-quality images, the observation models characterizing the image degradation processes have been largely ignored. In this paper, we first propose a denoising-based IR algorithm, whose iterative steps can be computed efficiently. Then, the iterative process is unfolded into a deep neural network, which is composed of multiple denoisers modules interleaved with back-projection (BP) modules that ensure the observation consistencies. A convolutional neural network (CNN) based denoiser that can exploit the multi-scale redundancies of natural images is proposed. As such, the proposed network not only exploits the powerful denoising ability of DNNs, but also leverages the prior of the observation model. Through end-to-end training, both the denoisers and the BP modules can be jointly optimized. Experimental results on several IR tasks, e.g., image denoising, super-resolution and deblurring show that the proposed method can lead to very competitive and often state-of-the-art results on several IR tasks, including image denoising, deblurring and super-resolution.

Connected through hyperedge HE-1 (induced by source paper "fast dense residual network enhancing global dense feature flow for text recognition"):
  with members:
    - "fast densenet towards efficient and accurate text recognition with fast dense networks"
    - "fully convolutional intensive feature flow neural network for text recognition"
    - "esir end to end scene text recognition via iterative image rectification"
    - "residual dense network for image super resolution"
    - "aon towards arbitrarily oriented text recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "reading scene text with attention convolutional sequence modeling"
    - "deep forest towards an alternative to deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "plug and play methods provably converge with properly trained denoisers"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "regularization by denoising clarifications and new interpretations"
    - "regularized fourier ptychography using an online plug and play algorithm"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "neural nearest neighbors networks"

Connected through hyperedge HE-3 (induced by source paper "integrating neural networks into the blind deblurring framework to compete with the end to end learning based methods"):
  with members:
    - "learning a discriminative prior for blind image deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "deep image matting"
    - "learning deep cnn denoiser prior for image restoration"
    - "detail revealing deep video super resolution"
    - "image restoration using autoencoding priors"
    - "video frame synthesis using deep voxel flow"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "iterative joint image demosaicking and denoising using a residual denoising network"):
  with members:
    - "on the convergence of adam and beyond"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "deep residual network for joint demosaicing and super resolution"
    - "learning deep cnn denoiser prior for image restoration"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "universal denoising networks a novel cnn architecture for image denoising"
    - "inexact proximal gradient methods for non convex and non smooth optimization"

Connected through hyperedge HE-5 (induced by source paper "residual dense network for image restoration"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "neural nearest neighbors networks"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "non local recurrent network for image restoration"
    - "density aware single image de raining using a multi stream dense network"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-6 (induced by source paper "deep graph laplacian regularization for robust denoising of real images"):
  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"
    - "learning to see in the dark"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "optnet differentiable optimization as a layer in neural networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-7 (induced by source paper "a novel extension of generalized low rank approximation of matrices based on multiple pairs of transformations"):
  with members:
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

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)