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: "video inpainting of complex scenes"
  Abstract: We propose an automatic video inpainting algorithm which relies on the optimization of a global, patch-based functional. Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects, and moving background. Furthermore, we achieve this in an order of magnitude less execution time with respect to the state-of-the-art. We are also able to achieve good quality results on high-definition videos. Finally, we provide specific algorithmic details to make implementation of our algorithm as easy as possible. The resulting algorithm requires no segmentation or manual input other than the definition of the inpainting mask and can deal with a wider variety of situations than is handled by previous work.

Connected through hyperedge HE-1 (induced by source paper "first image then video a two stage network for spatiotemporal video denoising"):
  with members:
    - "deep video inpainting"
    - "noisy as clean learning unsupervised denoising from the corrupted image"
    - "noise2self blind denoising by self supervision"
    - "videnn deep blind video denoising"
    - "fastdvdnet towards real time video denoising without explicit motion estimation"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "non local video denoising by cnn"

Connected through hyperedge HE-2 (induced by source paper "zero shot generation of human object interaction videos"):
  with members:
    - "video relationship reasoning using gated spatio temporal energy graph"
    - "cgans with projection discriminator"
    - "recycle gan unsupervised video retargeting"
    - "spectral normalization for generative adversarial networks"
    - "probabilistic video generation using holistic attribute control"
    - "large scale gan training for high fidelity natural image synthesis"
    - "videos as space time region graphs"
    - "image generation from scene graphs"

Connected through hyperedge HE-3 (induced by source paper "region wise generative adversarial imageinpainting for large missing areas"):
  with members:
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "pluralistic image completion"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "an empirical study on evaluation metrics of generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "free form video inpainting with 3d gated convolution and temporal patchgan"):
  with members:
    - "deep video inpainting"
    - "learnable gated temporal shift module for deep video inpainting"
    - "vornet spatio temporally consistent video inpainting for object removal"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "video to video synthesis"
    - "image inpainting for irregular holes using partial convolutions"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "learnable gated temporal shift module for deep video inpainting"):
  with members:
    - "free form video inpainting with 3d gated convolution and temporal patchgan"
    - "vornet spatio temporally consistent video inpainting for object removal"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "temporal bilinear networks for video action recognition"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-6 (induced by source paper "vornet spatio temporally consistent video inpainting for object removal"):
  with members:
    - "image inpainting for irregular holes using partial convolutions"
    - "learning blind video temporal consistency"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "shift net image inpainting via deep feature rearrangement"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "youtube vos a large scale video object segmentation benchmark"

Connected through hyperedge HE-7 (induced by source paper "autoremover automatic object removal for autonomous driving videos"):
  with members:
    - "frame recurrent video inpainting by robust optical flow inference"
    - "deep flow guided video inpainting"
    - "deep video inpainting"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "free form video inpainting with 3d gated convolution and temporal patchgan"
    - "aads augmented autonomous driving simulation using data driven algorithms"
    - "free form image inpainting with gated convolution"
    - "video inpainting by jointly learning temporal structure and spatial details"

Connected through hyperedge HE-8 (induced by source paper "an internal learning approach to video inpainting"):
  with members:
    - "deep flow guided video inpainting"
    - "deep video inpainting"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "everybody dance now"
    - "generative image inpainting with contextual attention"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

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)