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: "real image denoising with feature attention"
  Abstract: Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling. To advance the practicability of denoising algorithms, this paper proposes a novel single-stage blind real image denoising network (RIDNet) by employing a modular architecture. We use a residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthermore, the evaluation in terms of quantitative metrics and visual quality on three synthetic and four real noisy datasets against 19 state-of-the-art algorithms demonstrate the superiority of our RIDNet.

Connected through hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "kindling the darkness a practical low light image enhancer"
    - "residual non local attention networks for image restoration"
    - "making convolutional networks shift invariant again"
    - "a deep journey into super resolution a survey"
    - "selective kernel networks"
    - "deep learning for image super resolution a survey"
    - "deep high resolution representation learning for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "geometric back projection network for point cloud classification"):
  with members:
    - "hierarchical back projection network for image super resolution"
    - "pyramnet point cloud pyramid attention network and graph embedding module for classification and segmentation"
    - "point attention network for semantic segmentation of 3d point clouds"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
    - "kpconv flexible and deformable convolution for point clouds"
    - "pu gan a point cloud upsampling adversarial network"

Connected through hyperedge HE-3 (induced by source paper "gated texture cnn for efficient and configurable image denoising"):
  with members:
    - "cfsnet toward a controllable feature space for image restoration"
    - "residual non local attention networks for image restoration"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "free form video inpainting with 3d gated convolution and temporal patchgan"
    - "multi level wavelet convolutional neural networks"
    - "residual dense network for image restoration"
    - "class aware fully convolutional gaussian and poisson denoising"

Connected through hyperedge HE-4 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "noise flow noise modeling with conditional normalizing flows"
    - "semantic image synthesis with spatially adaptive normalization"
    - "suppressing model overfitting for image super resolution networks"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "iterative residual cnns for burst photography applications"

Connected through hyperedge HE-5 (induced by source paper "restore from restored video restoration with pseudo clean video"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "residual non local attention networks for image restoration"
    - "noise2self blind denoising by self supervision"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "frame recurrent video super resolution"
    - "residual dense network for image restoration"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "blockcnn a deep network for artifact removal and image compression"

Connected through hyperedge HE-6 (induced by source paper "stochastic frequency masking to improve super resolution and denoising networks"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "noise2self blind denoising by self supervision"
    - "zoom to learn learn to zoom"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "noise2void learning denoising from single noisy images"

Connected through hyperedge HE-7 (induced by source paper "self supervised fast adaptation for denoising via meta learning"):
  with members:
    - "residual non local attention networks for image restoration"
    - "noise2self blind denoising by self supervision"
    - "residual dense network for image restoration"
    - "on first order meta learning algorithms"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"

Connected through hyperedge HE-8 (induced by source paper "restore from restored single image denoising with pseudo clean image"):
  with members:
    - "residual non local attention networks for image restoration"
    - "noise2self blind denoising by self supervision"
    - "residual dense network for image restoration"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "non local recurrent network for image restoration"

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)