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: "demoireing of camera captured screen images using deep convolutional neural network"
  Abstract: Taking photos of optoelectronic displays is a direct and spontaneous way of transferring data and keeping records, which is widely practiced. However, due to the analog signal interference between the pixel grids of the display screen and camera sensor array, objectionable moir\'e (alias) patterns appear in captured screen images. As the moir\'e patterns are structured and highly variant, they are difficult to be completely removed without affecting the underneath latent image. In this paper, we propose an approach of deep convolutional neural network for demoir\'eing screen photos. The proposed DCNN consists of a coarse-scale network and a fine-scale network. In the coarse-scale network, the input image is first downsampled and then processed by stacked residual blocks to remove the moir\'e artifacts. After that, the fine-scale network upsamples the demoir\'ed low-resolution image back to the original resolution. Extensive experimental results have demonstrated that the proposed technique can efficiently remove the moir\'e patterns for camera acquired screen images; the new technique outperforms the existing ones.

Connected through hyperedge HE-1 (induced by source paper "multi scale dynamic feature encoding network for image demoireing"):
  with members:
    - "moire photo restoration using multiresolution convolutional neural networks"
    - "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:
    - "moire photo restoration using multiresolution convolutional neural networks"
    - "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.MM(Multimedia)