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: "cas cnn a deep convolutional neural network for image compression artifact suppression"
  Abstract: Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high compression rate with visual artifacts degrading the user experience. Deep convolutional neural networks have become a widespread tool to address high-level computer vision tasks very successfully. Recently, they have found their way into the areas of low-level computer vision and image processing to solve regression problems mostly with relatively shallow networks. We present a novel 12-layer deep convolutional network for image compression artifact suppression with hierarchical skip connections and a multi-scale loss function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an improvement of up to 0.36 dB over the best previous ConvNet result. We show that a network trained for a specific quality factor (QF) is resilient to the QF used to compress the input image — a single network trained for QF 60 provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.

Connected through hyperedge HE-1 (induced by source paper "deep learning based video coding a review and a case study"):
  with members:
    - "learning for video compression"
    - "image and video compression with neural networks a review"
    - "enhancing quality for hevc compressed videos"
    - "on the classification distortion perception tradeoff"
    - "learning based facial image compression with semantic fidelity metric"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "learned scalable image compression with bidirectional context disentanglement network"
    - "enlarging context with low cost efficient arithmetic coding with trimmed convolution"

Connected through hyperedge HE-2 (induced by source paper "image and video compression with neural networks a review"):
  with members:
    - "learning for video compression"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "s net a scalable convolutional neural network for jpeg compression artifact reduction"
    - "reducing complexity of hevc a deep learning approach"
    - "multi frame quality enhancement for compressed video"
    - "spatially adaptive image compression using a tiled deep network"
    - "a practical convolutional neural network as loop filter for intra frame"
    - "convolutional neural network based block up sampling for intra frame coding"

Connected through hyperedge HE-3 (induced by source paper "mfqe 2 0 a new approach for multi frame quality enhancement on compressed video"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "multi frame quality enhancement for compressed video"
    - "saliency guided complexity control for hevc decoding"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "detail revealing deep video super resolution"
    - "end to end learning of video super resolution with motion compensation"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-4 (induced by source paper "deep residual autoencoder for quality independent jpeg restoration"):
  with members:
    - "s net a scalable convolutional neural network for jpeg compression artifact reduction"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "enhanced deep residual networks for single image super resolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep generative adversarial compression artifact removal"
    - "image to image translation with conditional adversarial networks"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"

Connected through hyperedge HE-5 (induced by source paper "aim 2019 challenge on image demoireing dataset and study"):
  with members:
    - "moire photo restoration using multiresolution convolutional neural networks"
    - "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"
    - "adversarial discriminative domain adaptation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "compressing weight updates for image artifacts removal neural networks"):
  with members:
    - "compressibility loss for neural network weights"
    - "fine pruning joint fine tuning and compression of a convolutional network with bayesian optimization"
    - "deep convolution networks for compression artifacts reduction"
    - "u net convolutional networks for biomedical image segmentation"
    - "compression artifacts reduction by a deep convolutional network"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-7 (induced by source paper "hyperdrive a multi chip systolically scalable binary weight cnn inference engine"):
  with members:
    - "mixed precision training of convolutional neural networks using integer operations"
    - "xnorbin a 95 top s w hardware accelerator for binary convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "extended bit plane compression for convolutional neural network accelerators"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "an iot endpoint system on chip for secure and energy efficient near sensor analytics"

Connected through hyperedge HE-8 (induced by source paper "coconet a deep neural network for mapping pixel coordinates to color values"):
  with members:
    - "spatially adaptive image compression using a tiled deep network"
    - "optimizing the trade off between single stage and two stage object detectors using image difficulty prediction"
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "learning to inpaint for image compression"
    - "full resolution image compression with recurrent neural networks"
    - "generic 3d convolutional fusion for image restoration"
    - "end to end optimized image compression"

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)