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: "spatially adaptive image compression using a tiled deep network"
  Abstract: Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images. Existing image compression algorithms based on neural networks learn quantized representations with a constant spatial bit rate across each image. While entropy coding introduces some spatial variation, traditional codecs have benefited significantly by explicitly adapting the bit rate based on local image complexity and visual saliency. This paper introduces an algorithm that combines deep neural networks with quality-sensitive bit rate adaptation using a tiled network. We demonstrate the importance of spatial context prediction and show improved quantitative (PSNR) and qualitative (subjective rater assessment) results compared to a non-adaptive baseline and a recently published image compression model based on fully-convolutional neural networks.

Connected through hyperedge HE-1 (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-2 (induced by source paper "image and video compression with neural networks a review"):
  with members:
    - "learning for video compression"
    - "joint autoregressive and hierarchical priors for learned image 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"
    - "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-3 (induced by source paper "coconet a deep neural network for mapping pixel coordinates to color values"):
  with members:
    - "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"
    - "learning to inpaint for image compression"
    - "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-4 (induced by source paper "joint autoregressive and hierarchical priors for learned image compression"):
  with members:
    - "conditional probability models for deep image compression"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "parallel multiscale autoregressive density estimation"
    - "learning to inpaint for image compression"
    - "full resolution image compression with recurrent neural networks"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-5 (induced by source paper "image dependent local entropy models for learned image compression"):
  with members:
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "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 optimized image compression"
    - "variable rate image compression with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"):
  with members:
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimized image compression"
    - "density modeling of images using a generalized normalization transformation"
    - "variable rate image compression with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)