human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "image and video compression with neural networks a review"):
  Abstract: In recent years, the image and video coding technologies have advanced by leaps and bounds. However, due to the popularization of image and video acquisition devices, the growth rate of image and video data is far beyond the improvement of the compression ratio. In particular, it has been widely recognized that there are increasing challenges of pursuing further coding performance improvement within the traditional hybrid coding framework. Deep convolution neural network (CNN) which makes the neural network resurge in recent years and has achieved great success in both artificial intelligent and signal processing fields, also provides a novel and promising solution for image and video compression. In this paper, we provide a systematic, comprehensive and up-to-date review of neural network based image and video compression techniques. The evolution and development of neural network based compression methodologies are introduced for images and video respectively. More specifically, the cutting-edge video coding techniques by leveraging deep learning and HEVC framework are presented and discussed, which promote the state-of-the-art video coding performance substantially. Moreover, the end-to-end image and video coding frameworks based on neural networks are also reviewed, revealing interesting explorations on next generation image and video coding frameworks/standards. The most significant research works on the image and video coding related topics using neural networks are highlighted, and future trends are also envisioned. In particular, the joint compression on semantic and visual information is tentatively explored to formulate high efficiency signal representation structure for both human vision and machine vision, which are the two dominant signal receptor in the age of artificial intelligence.

Includes members:
  Member "learning for video compression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning based video coding a review and a case study")
  Member "joint autoregressive and hierarchical priors for learned image compression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical auto regressive model for image compression incorporating object saliency and a deep perceptual loss")
  Member "s net a scalable convolutional neural network for jpeg compression artifact reduction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep residual autoencoder for quality independent jpeg restoration")
  Member "reducing complexity of hevc a deep learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning based video coding a review and a case study")
  Member "multi frame quality enhancement for compressed video":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a foreground background parallel compression with residual encoding for surveillance video")
  Member "spatially adaptive image compression using a tiled deep network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning based video coding a review and a case study")
  Member "a practical convolutional neural network as loop filter for intra frame":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning based video coding a review and a case study")
  Member "convolutional neural network based block up sampling for intra frame coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning for video compression")

Question: Please tell me which class the source paper 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)