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: "training deep learning based denoisers without ground truth data"
  Abstract: Recent deep learning based denoisers are trained to minimize the mean squared error (MSE) between the output of a network and the ground truth noiseless image in the training data. Thus, it is crucial to have high quality noiseless training data for high performance denoisers. Unfortunately, in some application areas such as medical imaging, it is expensive or even infeasible to acquire such a clean ground truth image. We propose a Stein's Unbiased Risk Estimator (SURE) based method for training deep learning based denoisers without ground truth data. We demonstrated that our SURE based method only with noisy input data was able to train CNN based denoising networks that yielded performance close to that of the original MSE based deep learning denoisers with ground truth data.

Connected through hyperedge HE-1 (induced by source paper "high quality self supervised deep image denoising"):
  with members:
    - "an approach to image denoising using manifold approximation without clean images"
    - "noise2self blind denoising by self supervision"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "boosting cnn beyond label in inverse problems"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "spectral normalization for generative adversarial networks"
    - "k space deep learning for accelerated mri"
    - "deep back projection networks for super resolution"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "riemannian approach to batch normalization"

Connected through hyperedge HE-3 (induced by source paper "degrees of freedom analysis of unrolled neural networks"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unrolled optimization with deep priors"
    - "recurrent inference machines for solving inverse problems"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "raisr rapid and accurate image super resolution"
    - "super resolution with deep convolutional sufficient statistics"

Connected through hyperedge HE-4 (induced by source paper "extending stein s unbiased risk estimator to train deep denoisers with correlated pairs of noisy images"):
  with members:
    - "unprocessing images for learned raw denoising"
    - "deep image prior"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"
    - "yolo9000 better faster stronger"
    - "non local color image denoising with convolutional neural networks"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "training and refining deep learning based denoisers without ground truth data"):
  with members:
    - "noise2noise learning image restoration without clean data"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "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"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-6 (induced by source paper "fully convolutional pixel adaptive image denoiser"):
  with members:
    - "noise2noise learning image restoration without clean data"
    - "non local recurrent network for image restoration"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "deep image prior"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "sketch based manga retrieval using manga109 dataset"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-7 (induced by source paper "training deep learning based image denoisers from undersampled measurements without ground truth and without image prior"):
  with members:
    - "lapran a scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction"
    - "noise2noise learning image restoration without clean data"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"

Connected through hyperedge HE-8 (induced by source paper "leveraging deep stein s unbiased risk estimator for unsupervised x ray denoising"):
  with members:
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "medical image denoising using convolutional denoising autoencoders"

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)