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: "generative adversarial networks for extreme learned image compression"
  Abstract: We propose a framework for extreme learned image compression based on Generative Adversarial Networks (GANs), obtaining visually pleasing images at significantly lower bitrates than previous methods. This is made possible through our GAN formulation of learned compression combined with a generator/decoder which operates on the full-resolution image and is trained in combination with a multi-scale discriminator. Additionally, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from a semantic label map extracted from the original image, therefore only requiring the storage of the preserved region and the semantic label map. A user study confirms that for low bitrates, our approach significantly outperforms state-of-the-art methods, saving up to 67% compared to the next-best method BPG.

Connected through hyperedge HE-1 (induced by source paper "learning to structure an image with few colors"):
  with members:
    - "machine vision guided 3d medical image compression for efficient transmission and accurate segmentation in the clouds"
    - "clustering with deep learning taxonomy and new methods"
    - "fast object detection in compressed video"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "hybrid scene compression for visual localization"
    - "compressed video action recognition"

Connected through hyperedge HE-2 (induced by source paper "unsupervised enhancement of real world depth images using tri cycle gan"):
  with members:
    - "s flow gan"
    - "rethinking lossy compression the rate distortion perception tradeoff"
    - "deep depth completion of a single rgb d image"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"
    - "depth map completion by jointly exploiting blurry color images and sparse depth maps"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-3 (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"
    - "joint autoregressive and hierarchical priors for 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-4 (induced by source paper "near lossless ell_ infty constrained image decompression via deep neural network"):
  with members:
    - "joint autoregressive and hierarchical priors for 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-5 (induced by source paper "privacy preserving image sharing via sparsifying layers on convolutional groups"):
  with members:
    - "learning to compress and search visual data in large scale systems"
    - "encryption then compression systems using grayscale based image encryption for jpeg images"
    - "context aware generative adversarial privacy"
    - "privacy preserving identification using sparse approximation with ambiguization"
    - "defeating image obfuscation with deep learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-6 (induced by source paper "object based image coding a learning driven revisit"):
  with members:
    - "joint autoregressive and hierarchical priors for 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-7 (induced by source paper "deep image compression using decoder side information"):
  with members:
    - "conditional probability models for deep image compression"
    - "video compression through image interpolation"
    - "fast image processing with fully convolutional networks"
    - "learning binary residual representations for domain specific video streaming"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimized image compression"
    - "variable rate image compression with recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "a foreground background parallel compression with residual encoding for surveillance video"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "stochastic adversarial video prediction"
    - "progressive spatial recurrent neural network for intra prediction"
    - "multi frame quality enhancement for compressed video"
    - "video compression through image interpolation"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"
    - "image segmentation in video sequences a probabilistic approach"

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)