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: "deep video inpainting"
  Abstract: Video inpainting aims to fill spatio-temporal holes with plausible content in a video. Despite tremendous progress of deep neural networks for image inpainting, it is challenging to extend these methods to the video domain due to the additional time dimension. In this work, we propose a novel deep network architecture for fast video inpainting. Built upon an image-based encoder-decoder model, our framework is designed to collect and refine information from neighbor frames and synthesize still-unknown regions. At the same time, the output is enforced to be temporally consistent by a recurrent feedback and a temporal memory module. Compared with the state-of-the-art image inpainting algorithm, our method produces videos that are much more semantically correct and temporally smooth. In contrast to the prior video completion method which relies on time-consuming optimization, our method runs in near real-time while generating competitive video results. Finally, we applied our framework to video retargeting task, and obtain visually pleasing results.

Connected through hyperedge HE-1 (induced by source paper "motion excited sampler video adversarial attack with sparked prior"):
  with members:
    - "sparse black box video attack with reinforcement learning"
    - "heuristic black box adversarial attacks on video recognition models"
    - "appending adversarial frames for universal video attack"
    - "sequence level semantics aggregation for video object detection"
    - "black box adversarial attacks on video recognition models"
    - "adversarial attacks for optical flow based action recognition classifiers"
    - "sparse adversarial perturbations for videos"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-2 (induced by source paper "efficient semantic video segmentation with per frame inference"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dynamic video segmentation network"
    - "deep spatio temporal random fields for efficient video segmentation"

Connected through hyperedge HE-3 (induced by source paper "first image then video a two stage network for spatiotemporal video denoising"):
  with members:
    - "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"
    - "context aware synthesis for video frame interpolation"

Connected through hyperedge HE-4 (induced by source paper "background matting"):
  with members:
    - "deep flow guided video inpainting"
    - "free form video inpainting with 3d gated convolution and temporal patchgan"
    - "inductive guided filter real time deep image matting with weakly annotated masks on mobile devices"
    - "indices matter learning to index for deep image matting"
    - "disentangled image matting"
    - "alphagan generative adversarial networks for natural image matting"
    - "deep image matting"
    - "designing effective inter pixel information flow for natural image matting"

Connected through hyperedge HE-5 (induced by source paper "preserving semantic and temporal consistency for unpaired video to video translation"):
  with members:
    - "inserting videos into videos"
    - "deep blind video decaptioning by temporal aggregation and recurrence"
    - "frame recurrent video super resolution"
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"
    - "learning blind video temporal consistency"
    - "adversarial scene editing automatic object removal from weak supervision"

Connected through hyperedge HE-6 (induced by source paper "deep long audio inpainting"):
  with members:
    - "adversarial generation of time frequency features with application in audio synthesis"
    - "free form video inpainting with 3d gated convolution and temporal patchgan"
    - "learnable gated temporal shift module for deep video inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "a context encoder for audio inpainting"
    - "efficient neural audio synthesis"
    - "speech commands a dataset for limited vocabulary speech recognition"

Connected through hyperedge HE-7 (induced by source paper "free form video inpainting with 3d gated convolution and temporal patchgan"):
  with members:
    - "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"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-8 (induced by source paper "hypercon image to video model transfer for video to video translation tasks"):
  with members:
    - "deep flow guided video inpainting"
    - "copy and paste networks for deep video inpainting"
    - "video to video synthesis"
    - "learning blind video temporal consistency"
    - "a temporally aware interpolation network for video frame inpainting"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "youtube vos sequence to sequence video object segmentation"

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)