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: "learning to inpaint for image compression"
  Abstract: We study the design of deep architectures for lossy image compression. We present two architectural recipes in the context of multi-stage progressive encoders and empirically demonstrate their importance on compression performance. Specifically, we show that: (a) predicting the original image data from residuals in a multi-stage progressive architecture facilitates learning and leads to improved performance at approximating the original content and (b) learning to inpaint (from neighboring image pixels) before performing compression reduces the amount of information that must be stored to achieve a high-quality approximation. Incorporating these design choices in a baseline progressive encoder yields an average reduction of over $60\%$ in file size with similar quality compared to the original residual encoder.

Connected through hyperedge HE-1 (induced by source paper "learning for video compression"):
  with members:
    - "learning based facial image compression with semantic fidelity metric"
    - "convolutional neural network based block up sampling for intra frame coding"
    - "an end to end compression framework based on convolutional neural networks"
    - "a convolutional neural network approach for half pel interpolation in video coding"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "neural network based arithmetic coding of intra prediction modes in hevc"
    - "full resolution image compression with recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "discernible compressed images via deep perception consistency"):
  with members:
    - "conditional probability models for deep image compression"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "full resolution image compression with recurrent neural networks"
    - "ssd single shot multibox detector"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"
    - "variable rate image compression with recurrent neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "learning based facial image compression with semantic fidelity metric"):
  with members:
    - "learning for video compression"
    - "conditional probability models for deep image compression"
    - "an end to end compression framework based on convolutional neural networks"
    - "guetzli perceptually guided jpeg encoder"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "learning convolutional networks for content weighted image compression"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimization of nonlinear transform codes for perceptual quality"

Connected through hyperedge HE-5 (induced by source paper "layered image compression using scalable auto encoder"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "end to end optimized image compression"
    - "tensorflow a system for large scale machine learning"
    - "density modeling of images using a generalized normalization transformation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (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"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "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"
    - "full resolution image compression with recurrent neural networks"
    - "generic 3d convolutional fusion for image restoration"
    - "end to end optimized image compression"

Connected through hyperedge HE-7 (induced by source paper "progressive spatial recurrent neural network for intra prediction"):
  with members:
    - "dmcnn dual domain multi scale convolutional neural network for compression artifacts removal"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "video compression through image interpolation"):
  with members:
    - "conditional probability models for deep image compression"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "compressed video action recognition"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"

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)