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: "video compression through image interpolation"
  Abstract: An ever increasing amount of our digital communication, media consumption, and content creation revolves around videos. We share, watch, and archive many aspects of our lives through them, all of which are powered by strong video compression. Traditional video compression is laboriously hand designed and hand optimized. This paper presents an alternative in an end-to-end deep learning codec. Our codec builds on one simple idea: Video compression is repeated image interpolation. It thus benefits from recent advances in deep image interpolation and generation. Our deep video codec outperforms today's prevailing codecs, such as H.261, MPEG-4 Part 2, and performs on par with H.264.

Connected through hyperedge HE-1 (induced by source paper "softmax splatting for video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "video enhancement with task oriented flow"
    - "phasenet for video frame interpolation"
    - "temporal interpolation as an unsupervised pretraining task for optical flow estimation"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning to synthesize motion blur"
    - "deep video color propagation"

Connected through hyperedge HE-2 (induced by source paper "deep image compression using decoder side information"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "conditional probability models for deep image compression"
    - "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-3 (induced by source paper "a foreground background parallel compression with residual encoding for surveillance video"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "generative adversarial networks for extreme learned image compression"
    - "stochastic adversarial video prediction"
    - "progressive spatial recurrent neural network for intra prediction"
    - "multi frame quality enhancement for compressed video"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"
    - "image segmentation in video sequences a probabilistic approach"

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"
    - "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-5 (induced by source paper "deep generative video compression"):
  with members:
    - "learning for video compression"
    - "joint autoregressive and hierarchical priors for learned image 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"
    - "self supervised visual planning with temporal skip connections"

Connected through hyperedge HE-6 (induced by source paper "deep generative models for distribution preserving lossy compression"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "towards image understanding from deep compression without decoding"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "end to end optimized speech coding with deep neural networks"
    - "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"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep generative adversarial compression artifact removal"

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)