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: "gif2video color dequantization and temporal interpolation of gif images"
  Abstract: Graphics Interchange Format (GIF) is a highly portable graphics format that is ubiquitous on the Internet. Despite their small sizes, GIF images often contain undesirable visual artifacts such as flat color regions, false contours, color shift, and dotted patterns. In this paper, we propose GIF2Video, the first learning-based method for enhancing the visual quality of GIFs in the wild. We focus on the challenging task of GIF restoration by recovering information lost in the three steps of GIF creation: frame sampling, color quantization, and color dithering. We first propose a novel CNN architecture for color dequantization. It is built upon a compositional architecture for multi-step color correction, with a comprehensive loss function designed to handle large quantization errors. We then adapt the SuperSlomo network for temporal interpolation of GIF frames. We introduce two large datasets, namely GIF-Faces and GIF-Moments, for both training and evaluation. Experimental results show that our method can significantly improve the visual quality of GIFs, and outperforms direct baseline and state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "two phase hair image synthesis by self enhancing generative model"):
  with members:
    - "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"
    - "mganet a robust model for quality enhancement of compressed video"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "when awgn based denoiser meets real noises"):
  with members:
    - "path restore learning network path selection for image restoration"
    - "real image denoising with feature attention"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "survey of face detection on low quality images"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "connecting image denoising and high level vision tasks via deep learning"
    - "external prior guided internal prior learning for real world noisy image denoising"

Connected through hyperedge HE-3 (induced by source paper "content aware unsupervised deep homography estimation"):
  with members:
    - "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"
    - "mganet a robust model for quality enhancement of compressed video"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (induced by source paper "frame recurrent video inpainting by robust optical flow inference"):
  with members:
    - "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"
    - "mganet a robust model for quality enhancement of compressed video"
    - "perceptual losses for real time style transfer and super resolution"
    - "semantic video segmentation by gated recurrent flow propagation"

Connected through hyperedge HE-5 (induced by source paper "semi supervised skin detection by network with mutual guidance"):
  with members:
    - "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"
    - "mganet a robust model for quality enhancement of compressed video"

Connected through hyperedge HE-6 (induced by source paper "siclope silhouette based clothed people"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "synthesizing images of humans in unseen poses"
    - "bodynet volumetric inference of 3d human body shapes"
    - "video based reconstruction of 3d people models"
    - "photo wake up 3d character animation from a single photo"
    - "view extrapolation of human body from a single image"
    - "3d human pose estimation in the wild by adversarial learning"
    - "a variational u net for conditional appearance and shape generation"

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)