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: "noisy as clean learning unsupervised denoising from the corrupted image"
  Abstract: In the past few years, supervised networks have achieved promising performance on image denoising. These methods learn image priors and synthetic noise statistics from plenty pairs of noisy and clean images. Recently, several unsupervised denoising networks are proposed only using external noisy images for training. However, the networks learned from external data inherently suffer from the domain gap dilemma, i.e., the image priors and noise statistics are very different between the training data and the corrupted test images. This dilemma becomes more clear when dealing with the signal dependent realistic noise in real photographs. In this work, we provide a statistically useful conclusion: it is possible to learn an unsupervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and clean images. This is achieved by proposing a "Noisy-As-Clean" strategy: taking the corrupted image as "clean" target and the simulated noisy images (based on the corrupted image) as inputs. Extensive experiments show that the unsupervised denoising networks learned with our "Noisy-As-Clean" strategy surprisingly outperforms previous supervised networks on removing several typical synthetic noise and realistic noise. The code will be publicly released.

Connected through hyperedge HE-1 (induced by source paper "first image then video a two stage network for spatiotemporal video denoising"):
  with members:
    - "deep video inpainting"
    - "noise2self blind denoising by self supervision"
    - "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-2 (induced by source paper "semi supervised self growing generative adversarial networks for image recognition"):
  with members:
    - "nlh a blind pixel level non local method for real world image denoising"
    - "ranet ranking attention network for fast video object segmentation"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "learning view specific deep networks for person re identification"
    - "real world noisy image denoising a new benchmark"
    - "video salient object detection via fully convolutional networks"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-3 (induced by source paper "star a structure and texture aware retinex model"):
  with members:
    - "nlh a blind pixel level non local method for real world image denoising"
    - "salient object detection a survey"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "simultaneous fidelity and regularization learning for image restoration"
    - "scaled simplex representation for subspace clustering"
    - "color constancy by gans an experimental survey"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "unsupervised natural image patch learning"

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)