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 convolutional networks for content weighted image compression"
  Abstract: Lossy image compression is generally formulated as a joint rate-distortion optimization to learn encoder, quantizer, and decoder. However, the quantizer is non-differentiable, and discrete entropy estimation usually is required for rate control. These make it very challenging to develop a convolutional network (CNN)-based image compression system. In this paper, motivated by that the local information content is spatially variant in an image, we suggest that the bit rate of the different parts of the image should be adapted to local content. And the content aware bit rate is allocated under the guidance of a content-weighted importance map. Thus, the sum of the importance map can serve as a continuous alternative of discrete entropy estimation to control compression rate. And binarizer is adopted to quantize the output of encoder due to the binarization scheme is also directly defined by the importance map. Furthermore, a proxy function is introduced for binary operation in backward propagation to make it differentiable. Therefore, the encoder, decoder, binarizer and importance map can be jointly optimized in an end-to-end manner by using a subset of the ImageNet database. In low bit rate image compression, experiments show that our system significantly outperforms JPEG and JPEG 2000 by structural similarity (SSIM) index, and can produce the much better visual result with sharp edges, rich textures, and fewer artifacts.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "deep optimized multiple description image coding via scalar quantization learning"):
  with members:
    - "dmcnn dual domain multi scale convolutional neural network for compression artifacts removal"
    - "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"
    - "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-3 (induced by source paper "object based image coding a learning driven revisit"):
  with members:
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "generative adversarial networks for extreme learned 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 "storage space allocation strategy for digital data with message importance"):
  with members:
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "importance of small probability events in big data information measures applications and challenges"
    - "pareto optimal data compression for binary classification tasks"
    - "lossy compression for compute and forward in limited backhaul uplink multicell processing"
    - "message importance measure and its application to minority subset detection in big data"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-5 (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-6 (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"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "neural image compression and explanation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining a black box by using a deep variational information bottleneck approach"
    - "global model interpretation via recursive partitioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-8 (induced by source paper "adaptive weighting multi field of view cnn for semantic segmentation in pathology"):
  with members:
    - "autofocus layer for semantic segmentation"
    - "improving whole slide segmentation through visual context a systematic study"
    - "switching convolutional neural network for crowd counting"
    - "squeeze and excitation networks"
    - "mixture of counting cnns adaptive integration of cnns specialized to specific appearance for crowd counting"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gland instance segmentation by deep multichannel side supervision"

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)