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: "external prior guided internal prior learning for real world noisy image denoising"
  Abstract: Most of existing image denoising methods learn image priors from either an external data or the noisy image itself to remove noise. However, priors learned from an external data may not be adaptive to the image to be denoised, while priors learned from the given noisy image may not be accurate due to the interference of corrupted noise. Meanwhile, the noise in real-world noisy images is very complex, which is hard to be described by simple distributions such as Gaussian distribution, making real-world noisy image denoising a very challenging problem. We propose to exploit the information in both external data and the given noisy image, and develop an external prior guided internal prior learning method for real-world noisy image denoising. We first learn external priors from an independent set of clean natural images. With the aid of learned external priors, we then learn internal priors from the given noisy image to refine the prior model. The external and internal priors are formulated as a set of orthogonal dictionaries to efficiently reconstruct the desired image. Extensive experiments are performed on several real-world noisy image datasets. The proposed method demonstrates highly competitive denoising performance, outperforming state-of-the-art denoising methods including those designed for real-world noisy images.

Connected through hyperedge HE-1 (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"
    - "real image denoising with feature attention"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "meta transfer learning for zero shot super resolution"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "singan learning a generative model from a single natural image"
    - "residual non local attention networks for image restoration"
    - "feedback network for image super resolution"
    - "on first order meta learning algorithms"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-3 (induced by source paper "human aware motion deblurring"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "gated fusion network for single image dehazing"
    - "scale recurrent network for deep image deblurring"
    - "image correction via deep reciprocating hdr transformation"
    - "deep semantic face deblurring"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "learning blind motion deblurring"

Connected through hyperedge HE-4 (induced by source paper "semi supervised self growing generative adversarial networks for image recognition"):
  with members:
    - "noisy as clean learning unsupervised denoising from the corrupted image"
    - "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"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-5 (induced by source paper "nlh a blind pixel level non local method for real world image denoising"):
  with members:
    - "progressive image deraining networks a better and simpler baseline"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "simultaneous fidelity and regularization learning for image restoration"
    - "toward convolutional blind denoising of real photographs"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "neural nearest neighbors networks"
    - "real world noisy image denoising a new benchmark"

Connected through hyperedge HE-6 (induced by source paper "noisy as clean learning unsupervised denoising from the corrupted image"):
  with members:
    - "noise2self blind denoising by self supervision"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"
    - "neural nearest neighbors networks"
    - "real world noisy image denoising a new benchmark"
    - "noise2noise learning image restoration without clean data"

Connected through hyperedge HE-7 (induced by source paper "when awgn based denoiser meets real noises"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "path restore learning network path selection for image restoration"
    - "real image denoising with feature attention"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "survey of face detection on low quality images"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "connecting image denoising and high level vision tasks via deep learning"

Connected through hyperedge HE-8 (induced by source paper "star a structure and texture aware retinex model"):
  with members:
    - "noisy as clean learning unsupervised denoising from the corrupted image"
    - "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"
    - "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)