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: "moire photo restoration using multiresolution convolutional neural networks"
  Abstract: Digital cameras and mobile phones enable us to conveniently record precious moments. While digital image quality is constantly being improved, taking high-quality photos of digital screens still remains challenging because the photos are often contaminated with moire patterns, a result of the interference between the pixel grids of the camera sensor and the device screen. Moire patterns can severely damage the visual quality of photos. However, few studies have aimed to solve this problem. In this paper, we introduce a novel multiresolution fully convolutional network for automatically removing moire patterns from photos. Since a moire pattern spans over a wide range of frequencies, our proposed network performs a nonlinear multiresolution analysis of the input image before computing how to cancel moire artefacts within every frequency band. We also create a large-scale benchmark data set with 1 00 000#N# +#N# image pairs for investigating and evaluating moire pattern removal algorithms. Our network achieves the state-of-the-art performance on this data set in comparison to existing learning architectures for image restoration problems.

Connected through hyperedge HE-1 (induced by source paper "multi scale dynamic feature encoding network for image demoireing"):
  with members:
    - "demoireing of camera captured screen images using deep convolutional neural network"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "zm net real time zero shot image manipulation network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "aim 2019 challenge on image demoireing dataset and study"):
  with members:
    - "demoireing of camera captured screen images using deep convolutional neural network"
    - "residual dense network for image super resolution"
    - "joint sub bands learning with clique structures for wavelet domain super resolution"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "adversarial discriminative domain adaptation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "u net convolutional networks for biomedical image segmentation"

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)