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: "neural network based arithmetic coding of intra prediction modes in hevc"
  Abstract: In both H.264 and HEVC, context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding method. CABAC relies on manually designed binarization processes as well as handcrafted context models, which may restrict the compression efficiency. In this paper, we propose an arithmetic coding strategy by training neural networks, and make preliminary studies on coding of the intra prediction modes in HEVC. Instead of binarization, we propose to directly estimate the probability distribution of the 35 intra prediction modes with the adoption of a multi-level arithmetic codec. Instead of handcrafted context models, we utilize convolutional neural network (CNN) to perform the probability estimation. Simulation results show that our proposed arithmetic coding leads to as high as 9.9% bits saving compared with CABAC.

Connected through hyperedge HE-1 (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"
    - "learning to inpaint for image compression"
    - "full resolution image compression with recurrent neural networks"

Connected through hyperedge HE-2 (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-3 (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"
    - "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"

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)