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: "joint autoregressive and hierarchical priors for learned image compression"
  Abstract: Recent models for learned image compression are based on autoencoders, learning approximately invertible mappings from pixels to a quantized latent representation. These are combined with an entropy model, a prior on the latent representation that can be used with standard arithmetic coding algorithms to yield a compressed bitstream. Recently, hierarchical entropy models have been introduced as a way to exploit more structure in the latents than simple fully factorized priors, improving compression performance while maintaining end-to-end optimization. Inspired by the success of autoregressive priors in probabilistic generative models, we examine autoregressive, hierarchical, as well as combined priors as alternatives, weighing their costs and benefits in the context of image compression. While it is well known that autoregressive models come with a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and, together, exploit the probabilistic structure in the latents better than all previous learned models. The combined model yields state-of-the-art rate--distortion performance, providing a 15.8% average reduction in file size over the previous state-of-the-art method based on deep learning, which corresponds to a 59.8% size reduction over JPEG, more than 35% reduction compared to WebP and JPEG2000, and bitstreams 8.4% smaller than BPG, the current state-of-the-art image codec. To the best of our knowledge, our model is the first learning-based method to outperform BPG on both PSNR and MS-SSIM distortion metrics.

Connected through hyperedge HE-1 (induced by source paper "hierarchical auto regressive model for image compression incorporating object saliency and a deep perceptual loss"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "generative adversarial networks for extreme learned image compression"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "the perception distortion tradeoff"
    - "conditional probability models for deep image compression"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "non local neural networks"
    - "soft to hard vector quantization for end to end learning compressible representations"

Connected through hyperedge HE-2 (induced by source paper "near lossless ell_ infty constrained image decompression via deep neural network"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "conditional probability models for deep image compression"
    - "deep back projection networks for super resolution"
    - "dilated deep residual network for image denoising"
    - "enhanced deep residual networks for single image super resolution"
    - "deep generative adversarial compression artifact removal"
    - "single image super resolution with dilated convolution based multi scale information learning inception module"
    - "learning convolutional networks for content weighted image compression"

Connected through hyperedge HE-3 (induced by source paper "object based image coding a learning driven revisit"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "learning convolutional networks for content weighted image compression"
    - "end to end optimized image compression"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "feature pyramid networks for object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (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"
    - "learned scalable image compression with bidirectional context disentanglement network"
    - "enlarging context with low cost efficient arithmetic coding with trimmed convolution"
    - "deep convolutional autoencoder based lossy image compression"

Connected through hyperedge HE-5 (induced by source paper "rethinking lossy compression the rate distortion perception tradeoff"):
  with members:
    - "rate distortion perception tradeoff of variable length source coding for general information sources"
    - "generative adversarial networks for extreme learned image compression"
    - "maintaining natural image statistics with the contextual loss"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "adversarial feedback loop"
    - "the perception distortion tradeoff"
    - "conditional probability models for deep image compression"

Connected through hyperedge HE-6 (induced by source paper "image and video compression with neural networks a review"):
  with members:
    - "learning for video 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"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"

Connected through hyperedge HE-7 (induced by source paper "deep generative video compression"):
  with members:
    - "learning for video compression"
    - "iterative amortized inference"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"
    - "stochastic dynamics for video infilling"
    - "video compression through image interpolation"
    - "self supervised visual planning with temporal skip connections"

Connected through hyperedge HE-8 (induced by source paper "learning content weighted deep image compression"):
  with members:
    - "efficient trimmed convolutional arithmetic encoding for lossless image compression"
    - "enlarging context with low cost efficient arithmetic coding with trimmed convolution"
    - "conditional probability models for deep image compression"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "enhanced deep residual networks for single image super resolution"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "learning convolutional networks for content weighted 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)