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: "raisr rapid and accurate image super resolution"
  Abstract: Given an image, we wish to produce an image of larger size with significantly more pixels and higher image quality. This is generally known as the Single Image Super-Resolution (SISR) problem. The idea is that with sufficient training data (corresponding pairs of low and high resolution images) we can learn set of filters (i.e. a mapping) that when applied to given image that is not in the training set, will produce a higher resolution version of it, where the learning is preferably low complexity. In our proposed approach, the run-time is more than one to two orders of magnitude faster than the best competing methods currently available, while producing results comparable or better than state-of-the-art. #R##N#A closely related topic is image sharpening and contrast enhancement, i.e., improving the visual quality of a blurry image by amplifying the underlying details (a wide range of frequencies). Our approach additionally includes an extremely efficient way to produce an image that is significantly sharper than the input blurry one, without introducing artifacts such as halos and noise amplification. We illustrate how this effective sharpening algorithm, in addition to being of independent interest, can be used as a pre-processing step to induce the learning of more effective upscaling filters with built-in sharpening and contrast enhancement effect.

Connected through hyperedge HE-1 (induced by source paper "super resolution via image adapted denoising cnns incorporating external and internal learning"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "class aware fully convolutional gaussian and poisson denoising"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "regularization by denoising clarifications and new interpretations"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "a fully progressive approach to single image super resolution"

Connected through hyperedge HE-2 (induced by source paper "deformable kernel networks for joint image filtering"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "learning guided convolutional network for depth completion"
    - "blade filter learning for general purpose computational photography"
    - "video frame interpolation via adaptive convolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "burst denoising with kernel prediction networks"
    - "joint image filtering with deep convolutional networks"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-3 (induced by source paper "basis prediction networks for effective burst denoising with large kernels"):
  with members:
    - "residual non local attention networks for image restoration"
    - "multi kernel prediction networks for denoising of burst images"
    - "handheld mobile photography in very low light"
    - "iterative residual cnns for burst photography applications"
    - "identifying recurring patterns with deep neural networks for natural image denoising"
    - "non local recurrent network for image restoration"
    - "learning to see in the dark"
    - "blade filter learning for general purpose computational photography"

Connected through hyperedge HE-4 (induced by source paper "pixel adaptive filtering units"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "iterative joint image demosaicking and denoising using a residual denoising network"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "neural nearest neighbors networks"
    - "segmentation aware convolutional networks using local attention masks"
    - "blade filter learning for general purpose computational photography"
    - "convolutional networks with adaptive inference graphs"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-5 (induced by source paper "degrees of freedom analysis of unrolled neural networks"):
  with members:
    - "training deep learning based denoisers without ground truth data"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unrolled optimization with deep priors"
    - "recurrent inference machines for solving inverse problems"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "super resolution with deep convolutional sufficient statistics"

Connected through hyperedge HE-6 (induced by source paper "convolutional neural networks analysed via inverse problem theory and sparse representations"):
  with members:
    - "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"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "learning to deblur"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "handheld multi frame super resolution"):
  with members:
    - "frame recurrent video super resolution"
    - "synthetic depth of field with a single camera mobile phone"
    - "deep burst denoising"
    - "burst denoising with kernel prediction networks"

Connected through hyperedge HE-8 (induced by source paper "neural proximal gradient descent for compressive imaging"):
  with members:
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "searching for activation functions"
    - "unrolled optimization with deep priors"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "diracnets training very deep neural networks without skip connections"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "perceptual losses for real time style transfer and super resolution"
    - "maximal sparsity with deep networks"

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)