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: "noise2self blind denoising by self supervision"
  Abstract: We propose a general framework for denoising high-dimensional measurements which requires no prior on the signal, no estimate of the noise, and no clean training data. The only assumption is that the noise exhibits statistical independence across different dimensions of the measurement, while the true signal exhibits some correlation. For a broad class of functions ("$\mathcal{J}$-invariant"), it is then possible to estimate the performance of a denoiser from noisy data alone. This allows us to calibrate $\mathcal{J}$-invariant versions of any parameterised denoising algorithm, from the single hyperparameter of a median filter to the millions of weights of a deep neural network. We demonstrate this on natural image and microscopy data, where we exploit noise independence between pixels, and on single-cell gene expression data, where we exploit independence between detections of individual molecules. This framework generalizes recent work on training neural nets from noisy images and on cross-validation for matrix factorization.

Connected through hyperedge HE-1 (induced by source paper "blur noise and compression robust generative adversarial networks"):
  with members:
    - "analyzing and improving the image quality of stylegan"
    - "noise robust generative adversarial networks"
    - "high quality self supervised deep image denoising"
    - "unsupervised domain specific deblurring via disentangled representations"
    - "diversity sensitive conditional generative adversarial networks"
    - "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"

Connected through hyperedge HE-2 (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"
    - "real image denoising with feature attention"
    - "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-3 (induced by source paper "stochastic frequency masking to improve super resolution and denoising networks"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "real image denoising with feature attention"
    - "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-4 (induced by source paper "first image then video a two stage network for spatiotemporal video denoising"):
  with members:
    - "deep video inpainting"
    - "noisy as clean learning unsupervised denoising from the corrupted image"
    - "videnn deep blind video denoising"
    - "fastdvdnet towards real time video denoising without explicit motion estimation"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "non local video denoising by cnn"
    - "context aware synthesis for video frame interpolation"

Connected through hyperedge HE-5 (induced by source paper "self supervised fast adaptation for denoising via meta learning"):
  with members:
    - "residual non local attention networks for image restoration"
    - "real image denoising with feature attention"
    - "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-6 (induced by source paper "restore from restored single image denoising with pseudo clean image"):
  with members:
    - "residual non local attention networks for image restoration"
    - "real image denoising with feature attention"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "noise robust generative adversarial networks"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "diversity sensitive conditional generative adversarial networks"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "self supervised gans via auxiliary rotation loss"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"

Connected through hyperedge HE-8 (induced by source paper "high quality self supervised deep image denoising"):
  with members:
    - "an approach to image denoising using manifold approximation without clean images"
    - "local kernels that approximate bayesian regularization and proximal operators"
    - "residual dense network for image restoration"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "fully convolutional pixel adaptive image denoiser"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"

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)