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 denoising using very deep fully convolutional encoder decoder networks with symmetric skip connections"
  Abstract: Image denoising is a long-standing problem in computer vision and image processing, as well as a test bed for low-level image modeling algorithms. In this paper, we propose a very deep encoding-decoding framework for image denoising. Instead of using image priors, the proposed framework learns end-to-end fully convolutional mappings from noisy images to the clean ones. The network is composed of multiple layers of convolution and de-convolution operators. With the observation that deeper networks improve denoising performance, we propose to use significantly deeper networks than those employed previously for low-level image processing tasks such as denoising. We propose to symmetrically link convolutional and de-convolutional layers with skip-layer connections, with which the training converges much faster and attains a higher-quality local optimum. From the image processing point of view, those symmetric connections help preserve image details.

Connected through hyperedge HE-1 (induced by source paper "videnn deep blind video denoising"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "phasenet for video frame interpolation"
    - "toward convolutional blind denoising of real photographs"
    - "noise2noise learning image restoration without clean data"
    - "renoir a dataset for real low light image noise reduction"
    - "context aware synthesis for video frame interpolation"
    - "learning to see in the dark"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-2 (induced by source paper "image deconvolution with deep image and kernel priors"):
  with members:
    - "deep image prior"
    - "learning deep cnn denoiser prior for image restoration"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "adam a method for stochastic optimization"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "multimedia super resolution via deep learning a survey"):
  with members:
    - "joint convolutional neural pyramid for depth map super resolution"
    - "learning a mixture of deep networks for single image super resolution"
    - "an analysis of deep neural network models for practical applications"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "failures of gradient based deep learning"
    - "enhanced deep residual networks for single image super resolution"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "single image super resolution when model adaptation matters"

Connected through hyperedge HE-4 (induced by source paper "learning warped guidance for blind face restoration"):
  with members:
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "deep face deblurring"
    - "generative face completion"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "motion deblurring in the wild"
    - "attention aware face hallucination via deep reinforcement learning"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "gp gan gender preserving gan for synthesizing faces from landmarks"):
  with members:
    - "every smile is unique landmark guided diverse smile generation"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "densely connected pyramid dehazing network"
    - "began boundary equilibrium generative adversarial networks"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "pose guided person image generation"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "natural and effective obfuscation by head inpainting"

Connected through hyperedge HE-6 (induced by source paper "face synthesis from visual attributes via sketch using conditional vaes and gans"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "cvae gan fine grained image generation through asymmetric training"
    - "perceptual losses for real time style transfer and super resolution"
    - "unrolled generative adversarial networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-7 (induced by source paper "a multiscale image denoising algorithm based on dilated residual convolution network"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "dilated deep residual network for image denoising"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "dilated residual networks"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "understanding convolution for semantic segmentation"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"

Connected through hyperedge HE-8 (induced by source paper "learning light field reconstruction from a single coded image"):
  with members:
    - "unsupervised monocular depth estimation with left right consistency"
    - "learning based view synthesis for light field cameras"
    - "deepstereo learning to predict new views from the world s imagery"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"
    - "sparse modeling for image and vision processing"

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)