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: "a group variational transformation neural network for fractional interpolation of video coding"
  Abstract: Motion compensation is an important technology in video coding to remove the temporal redundancy between coded video frames. In motion compensation, fractional interpolation is used to obtain more reference blocks at sub-pixel level. Existing video coding standards commonly use fixed interpolation filters for fractional interpolation, which are not efficient enough to handle diverse video signals well. In this paper, we design a group variational transformation convolutional neural network (GVTCNN) to improve the fractional interpolation performance of the luma component in motion compensation. GVTCNN infers samples at different sub-pixel positions from the input integer-position sample. It first extracts a shared feature map from the integer-position sample to infer various sub-pixel position samples. Then a group variational transformation technique is used to transform a group of copied shared feature maps to samples at different sub-pixel positions. Experimental results have identified the interpolation efficiency of our GVTCNN. Compared with the interpolation method of High Efficiency Video Coding, our method achieves 1.9% bit saving on average and up to 5.6% bit saving under low-delay P configuration.

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"
    - "temporal bilinear networks for video action recognition"
    - "context adaptive neural network based prediction for image compression"
    - "deep retinex decomposition for low light enhancement"
    - "efficient multiple line based intra prediction for hevc"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "dmcnn dual domain multi scale convolutional neural network for compression artifacts removal"):
  with members:
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "multi scale context aggregation by dilated convolutions"

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)