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: "mganet a robust model for quality enhancement of compressed video"
  Abstract: In video compression, most of the existing deep learning approaches concentrate on the visual quality of a single frame, while ignoring the useful priors as well as the temporal information of adjacent frames. In this paper, we propose a multi-frame guided attention network (MGANet) to enhance the quality of compressed videos. Our network is composed of a temporal encoder that discovers inter-frame relations, a guided encoder-decoder subnet that encodes and enhances the visual patterns of target frame, and a multi-supervised reconstruction component that aggregates information to predict details. We design a bidirectional residual convolutional LSTM unit to implicitly discover frames variations over time with respect to the target frame. Meanwhile, the guided map is proposed to guide our network to concentrate more on the block boundary. Our approach takes advantage of intra-frame prior information and inter-frame information to improve the quality of compressed video. Experimental results show the robustness and superior performance of the proposed method.Code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "two phase hair image synthesis by self enhancing generative model"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "faceshop deep sketch based face image editing"
    - "learning dual convolutional neural networks for low level vision"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "residual dense network for image super resolution"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "content aware unsupervised deep homography estimation"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "frame recurrent video inpainting by robust optical flow inference"
    - "semi supervised video salient object detection using pseudo labels"
    - "two phase hair image synthesis by self enhancing generative model"
    - "learning raw image denoising with bayer pattern unification and bayer preserving augmentation"
    - "semi supervised skin detection by network with mutual guidance"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "frame recurrent video inpainting by robust optical flow inference"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "identity preserving face completion for large ocular region occlusion"
    - "learning blind video temporal consistency"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "deep temporal lstm for daily living action recognition"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "perceptual losses for real time style transfer and super resolution"
    - "semantic video segmentation by gated recurrent flow propagation"

Connected through hyperedge HE-4 (induced by source paper "semi supervised skin detection by network with mutual guidance"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "frame recurrent video inpainting by robust optical flow inference"
    - "two phase hair image synthesis by self enhancing generative model"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "video inpainting by jointly learning temporal structure and spatial details"

Connected through hyperedge HE-5 (induced by source paper "enhancing quality for vvc compressed videos by jointly exploiting spatial details and temporal structure"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "multi frame quality enhancement for compressed video"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "squeeze and excitation networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"
    - "real time video super resolution with spatio temporal networks and motion compensation"

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.CV(Computer Vision and Pattern Recognition)