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 for video compression"
  Abstract: One key challenge to learning-based video compression is that motion predictive coding, a very effective tool for video compression, can hardly be trained into a neural network. In this paper, we propose the concept of PixelMotionCNN (PMCNN) which includes motion extension and hybrid prediction networks. PMCNN can model spatiotemporal coherence to effectively perform predictive coding inside the learning network. On the basis of PMCNN, we further explore a learning-based framework for video compression with additional components of iterative analysis/synthesis and binarization. The experimental results demonstrate the effectiveness of the proposed scheme. Although entropy coding and complex configurations are not employed in this paper, we still demonstrate superior performance compared with MPEG-2 and achieve comparable results with H.264 codec. The proposed learning-based scheme provides a possible new direction to further improve compression efficiency and functionalities of future video coding.

Connected through hyperedge HE-1 (induced by source paper "deep learning based video coding a review and a case study"):
  with members:
    - "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"
    - "deep convolutional autoencoder based lossy image compression"

Connected through hyperedge HE-2 (induced by source paper "image and video compression with neural networks a review"):
  with members:
    - "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"
    - "spatially adaptive image compression using a tiled deep network"
    - "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 "deep generative video compression"):
  with members:
    - "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"
    - "video compression through image interpolation"
    - "self supervised visual planning with temporal skip connections"

Connected through hyperedge HE-4 (induced by source paper "learning based facial image compression with semantic fidelity metric"):
  with members:
    - "conditional probability models for deep image compression"
    - "an end to end compression framework based on convolutional neural networks"
    - "guetzli perceptually guided jpeg encoder"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "learning to inpaint for image compression"
    - "learning convolutional networks for content weighted image compression"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimization of nonlinear transform codes for perceptual quality"

Connected through hyperedge HE-5 (induced by source paper "deep probabilistic video compression"):
  with members:
    - "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"
    - "advances in variational inference"

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.MM(Multimedia)