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: "dmcnn dual domain multi scale convolutional neural network for compression artifacts removal"
  Abstract: JPEG is one of the most commonly used standards among lossy image compression methods. However, JPEG compression inevitably introduces various kinds of artifacts, especially at high compression rates, which could greatly affect the Quality of Experience (QoE). Recently, convolutional neural network (CNN) based methods have shown excellent performance for removing the JPEG artifacts. Lots of efforts have been made to deepen the CNNs and extract deeper features, while relatively few works pay attention to the receptive field of the network. In this paper, we illustrate that the quality of output images can be significantly improved by enlarging the receptive fields in many cases. One step further, we propose a Dual-domain Multi-scale CNN (DMCNN) to take full advantage of redundancies on both the pixel and DCT domains. Experiments show that DMCNN sets a new state-of-the-art for the task of JPEG artifact removal.

Connected through hyperedge HE-1 (induced by source paper "deep optimized multiple description image coding via scalar quantization learning"):
  with members:
    - "the perception distortion tradeoff"
    - "conditional probability models for deep image compression"
    - "multiple description convolutional neural networks for image compression"
    - "learning a virtual codec based on deep convolutional neural network to compress image"
    - "understanding convolution for semantic segmentation"
    - "learning convolutional networks for content weighted image compression"
    - "an achievable rate distortion region for multiple descriptions source coding based on coset codes"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "disentangled image matting"):
  with members:
    - "alphagan generative adversarial networks for natural image matting"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "semantic human matting"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep image matting"
    - "designing effective inter pixel information flow for natural image matting"

Connected through hyperedge HE-3 (induced by source paper "progressive spatial recurrent neural network for intra prediction"):
  with members:
    - "a group variational transformation neural network for fractional interpolation of video coding"
    - "temporal bilinear networks for video action recognition"
    - "context adaptive neural network based prediction for image compression"
    - "deep retinex decomposition for low light enhancement"
    - "efficient multiple line based intra prediction for hevc"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep edge guided recurrent residual learning for image super resolution"
    - "soft to hard vector quantization for end to end learning compressible representations"

Connected through hyperedge HE-4 (induced by source paper "implicit dual domain convolutional network for robust color image compression artifact reduction"):
  with members:
    - "residual dense network for image restoration"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "s net a scalable convolutional neural network for jpeg compression artifact reduction"
    - "dpw sdnet dual pixel wavelet domain deep cnns for soft decoding of jpeg compressed images"
    - "multi level wavelet cnn for image restoration"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-5 (induced by source paper "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "polynet a pursuit of structural diversity in very deep networks"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "compression artifacts reduction by a deep convolutional network"

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)