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 plug and play super resolution for arbitrary blur kernels"
  Abstract: While deep neural networks (DNN) based single image super-resolution (SISR) methods are rapidly gaining popularity, they are mainly designed for the widely-used bicubic degradation, and there still remains the fundamental challenge for them to super-resolve low-resolution (LR) image with arbitrary blur kernels. In the meanwhile, plug-and-play image restoration has been recognized with high flexibility due to its modular structure for easy plug-in of denoiser priors. In this paper, we propose a principled formulation and framework by extending bicubic degradation based deep SISR with the help of plug-and-play framework to handle LR images with arbitrary blur kernels. Specifically, we design a new SISR degradation model so as to take advantage of existing blind deblurring methods for blur kernel estimation. To optimize the new degradation induced energy function, we then derive a plug-and-play algorithm via variable splitting technique, which allows us to plug any super-resolver prior rather than the denoiser prior as a modular part. Quantitative and qualitative evaluations on synthetic and real LR images demonstrate that the proposed deep plug-and-play super-resolution framework is flexible and effective to deal with blurry LR images.

Connected through hyperedge HE-1 (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"
    - "real image denoising with feature attention"
    - "zoom to learn learn to zoom"
    - "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-2 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "joint enhancement and denoising method via sequential decomposition"
    - "getting to know low light images with the exclusively dark dataset"
    - "yolov3 an incremental improvement"
    - "pyramidbox a context assisted single shot face detector"
    - "real world noisy image denoising a new benchmark"

Connected through hyperedge HE-3 (induced by source paper "learned image downscaling for upscaling using content adaptive resampler"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "a deep journey into super resolution a survey"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "image super resolution using very deep residual channel attention networks"
    - "ram residual attention module for single image super resolution"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "learning a single convolutional super resolution network for multiple degradations"

Connected through hyperedge HE-4 (induced by source paper "deep plug and play prior for low rank tensor completion"):
  with members:
    - "plug and play methods provably converge with properly trained denoisers"
    - "low rank tensor completion for multiway visual data"
    - "regularization by denoising clarifications and new interpretations"
    - "non local meets global an integrated paradigm for hyperspectral denoising"
    - "tensor n tubal rank and its convex relaxation for low rank tensor recovery"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "image restoration by iterative denoising and backward projections"
    - "learning deep cnn denoiser prior for image restoration"

Connected through hyperedge HE-5 (induced by source paper "investigating task driven latent feasibility for nonconvex image modeling"):
  with members:
    - "fastderain a novel video rain streak removal method using directional gradient priors"
    - "plug and play methods provably converge with properly trained denoisers"
    - "regularization by denoising clarifications and new interpretations"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "acceleration of red via vector extrapolation"
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective 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)