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: "deep edge guided recurrent residual learning for image super resolution"
  Abstract: In this paper, we consider the image super-resolution (SR) problem. The main challenge of image SR is to recover high-frequency details of a low-resolution (LR) image that are important for human perception. To address this essentially ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual (DEGREE) network to progressively recover the high-frequency details. Different from most of the existing methods that aim at predicting high-resolution (HR) images directly, the DEGREE investigates an alternative route to recover the difference between a pair of LR and HR images by recurrent residual learning. DEGREE further augments the SR process with edge-preserving capability, namely the LR image and its edge map can jointly infer the sharp edge details of the HR image during the recurrent recovery process. To speed up its training convergence rate, by-pass connections across the multiple layers of DEGREE are constructed. In addition, we offer an understanding on DEGREE from the view-point of sub-band frequency decomposition on image signal and experimentally demonstrate how the DEGREE can recover different frequency bands separately. Extensive experiments on three benchmark data sets clearly demonstrate the superiority of DEGREE over the well-established baselines and DEGREE also provides new state-of-the-arts on these data sets. We also present addition experiments for JPEG artifacts reduction to demonstrate the good generality and flexibility of our proposed DEGREE network to handle other image processing tasks.

Connected through hyperedge HE-1 (induced by source paper "deep learning for single image super resolution a brief review"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "fast and accurate single image super resolution via information distillation network"
    - "super resolution based on image adapted cnn denoisers incorporating generalization of training data and internal learning in test time"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "the perception distortion tradeoff"
    - "super resolution via conditional implicit maximum likelihood estimation"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-2 (induced by source paper "drfn deep recurrent fusion network for single image super resolution with large factors"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "structure preserving image super resolution via contextualized multitask learning"
    - "enhanced deep residual networks for single image super resolution"
    - "memnet a persistent memory network for image restoration"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"

Connected through hyperedge HE-3 (induced by source paper "semantic edge detection with diverse deep supervision"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "deep crisp boundaries from boundaries to higher level tasks"
    - "dynamic feature fusion for semantic edge detection"
    - "deeply supervised salient object detection with short connections"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "taskonomy disentangling task transfer learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "simultaneous edge alignment and learning"

Connected through hyperedge HE-4 (induced by source paper "an attention based approach for single image super resolution"):
  with members:
    - "salient object detection by lossless feature reflection"
    - "weakly supervised salient object detection using image labels"
    - "attention is all you need"
    - "enhanced deep residual networks for single image super resolution"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "progressive spatial recurrent neural network for intra prediction"):
  with members:
    - "dmcnn dual domain multi scale convolutional neural network for compression artifacts removal"
    - "a group variational transformation neural network for fractional interpolation of video coding"
    - "temporal bilinear networks for video action recognition"
    - "context adaptive neural network based prediction for image compression"
    - "deep retinex decomposition for low light enhancement"
    - "efficient multiple line based intra prediction for hevc"
    - "in datacenter performance analysis of a tensor processing unit"
    - "soft to hard vector quantization for end to end learning compressible representations"

Connected through hyperedge HE-6 (induced by source paper "sredgenet edge enhanced single image super resolution using dense edge detection network and feature merge network"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "connecting image denoising and high level vision tasks via deep learning"
    - "residual dense network for image super resolution"
    - "richer convolutional features for edge detection"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "holistically nested edge detection"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "a group variational transformation neural network for fractional interpolation of video coding"):
  with members:
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "accurate image super resolution using very deep convolutional networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "learning a single convolutional super resolution network for multiple degradations"):
  with members:
    - "residual dense network for image super resolution"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "structure preserving image super resolution via contextualized multitask learning"
    - "enhanced deep residual networks for single image super resolution"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"
    - "perceptual losses for real time style transfer and super resolution"

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)