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: "end to end optimized image compression"
  Abstract: We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation. The transforms are constructed in three successive stages of convolutional linear filters and nonlinear activation functions. Unlike most convolutional neural networks, the joint nonlinearity is chosen to implement a form of local gain control, inspired by those used to model biological neurons. Using a variant of stochastic gradient descent, we jointly optimize the entire model for rate-distortion performance over a database of training images, introducing a continuous proxy for the discontinuous loss function arising from the quantizer. Under certain conditions, the relaxed loss function may be interpreted as the log likelihood of a generative model, as implemented by a variational autoencoder. Unlike these models, however, the compression model must operate at any given point along the rate-distortion curve, as specified by a trade-off parameter. Across an independent set of test images, we find that the optimized method generally exhibits better rate-distortion performance than the standard JPEG and JPEG 2000 compression methods. More importantly, we observe a dramatic improvement in visual quality for all images at all bit rates, which is supported by objective quality estimates using MS-SSIM.

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"
    - "generative adversarial networks for extreme learned image compression"
    - "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"

Connected through hyperedge HE-2 (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"
    - "learning to inpaint for image compression"

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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment"):
  with members:
    - "i am going mad maximum discrepancy competition for comparing classifiers adaptively"
    - "learning to blindly assess image quality in the laboratory and wild"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "nima neural image assessment"
    - "on the use of deep learning for blind image quality assessment"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"

Connected through hyperedge HE-5 (induced by source paper "predictive sampling with forecasting autoregressive models"):
  with members:
    - "pixelvae improved pixelvae with discrete prior"
    - "generating long sequences with sparse transformers"
    - "pytorch an imperative style high performance deep learning library"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "fast generation for convolutional autoregressive models"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "spatial pixelcnn generating images from patches"
    - "neural discrete representation learning"

Connected through hyperedge HE-6 (induced by source paper "be like water robustness to extraneous variables via adaptive feature normalization"):
  with members:
    - "filter response normalization layer eliminating batch dependence in the training of deep neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "adversarial discriminative domain adaptation"
    - "instance normalization the missing ingredient for fast stylization"
    - "deep transfer learning with joint adaptation networks"
    - "densely connected convolutional networks"
    - "unsupervised domain adaptation with residual transfer networks"

Connected through hyperedge HE-7 (induced by source paper "deep joint transmission recognition for power constrained iot devices"):
  with members:
    - "bottlenet an end to end approach for feature compression in device edge co inference systems"
    - "deep joint source channel coding for wireless image retrieval"
    - "improving device edge cooperative inference of deep learning via 2 step pruning"
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "deepjscc f deep joint source channel coding of images with feedback"
    - "deep joint source channel coding for wireless image transmission"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-8 (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"
    - "learning convolutional networks for content weighted 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"

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)