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: "efficient multiple line based intra prediction for hevc"
  Abstract: Traditional intra prediction usually utilizes the nearest reference line to generate the predicted block when considering strong spatial correlation. However, this kind of single-line-based method does not always work well due to at least two issues. One is the incoherence caused by the signal noise or the texture of other objects, where this texture deviates from the inherent texture of the current block. The other reason is that the nearest reference line usually has worse reconstruction quality in block-based video coding. Due to these two issues, this paper proposes an efficient multiple-line-based intra-prediction scheme to improve coding efficiency. Besides the nearest reference line, further reference lines are also utilized. The further reference lines with a relatively higher quality can provide potentially better prediction. At the same time, the residue compensation is introduced to calibrate the prediction of boundary regions in a block when we utilize further reference lines. To speed up the encoding process, this paper designs several fast algorithms. The experimental results show that compared with HM-16.9, the proposed fast search method achieves a 2.0% bit saving on average and up to 3.7% by increasing the encoding time by 112%.

Connected through hyperedge HE-1 (induced by source paper "progressive spatial recurrent neural network for intra prediction"):
  with members:
    - "dmcnn dual domain multi scale convolutional neural network for compression artifacts removal"
    - "a group variational transformation neural network for fractional interpolation of video coding"
    - "temporal bilinear networks for video action recognition"
    - "context adaptive neural network based prediction for image compression"
    - "deep retinex decomposition for low light enhancement"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep edge guided recurrent residual learning for image super resolution"
    - "soft to hard vector quantization for end to end learning compressible representations"

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)