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 flow guided video inpainting"
  Abstract: Video inpainting, which aims at filling in missing regions of a video, remains challenging due to the difficulty of preserving the precise spatial and temporal coherence of video contents. In this work we propose a novel flow-guided video inpainting approach. Rather than filling in the RGB pixels of each frame directly, we consider video inpainting as a pixel propagation problem. We first synthesize a spatially and temporally coherent optical flow field across video frames using a newly designed Deep Flow Completion network. Then the synthesized flow field is used to guide the propagation of pixels to fill up the missing regions in the video. Specifically, the Deep Flow Completion network follows a coarse-to-fine refinement to complete the flow fields, while their quality is further improved by hard flow example mining. Following the guide of the completed flow, the missing video regions can be filled up precisely. Our method is evaluated on DAVIS and YouTube-VOS datasets qualitatively and quantitatively, achieving the state-of-the-art performance in terms of inpainting quality and speed.

Connected through hyperedge HE-1 (induced by source paper "background matting"):
  with members:
    - "deep 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-2 (induced by source paper "carafe content aware reassembly of features"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "meta sr a magnification arbitrary network for super resolution"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "foreground aware image inpainting"
    - "mask scoring r cnn"
    - "guided upsampling network for real time semantic segmentation"
    - "deep feature pyramid reconfiguration for object detection"

Connected through hyperedge HE-3 (induced by source paper "hypercon image to video model transfer for video to video translation tasks"):
  with members:
    - "deep 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"

Connected through hyperedge HE-4 (induced by source paper "autoremover automatic object removal for autonomous driving videos"):
  with members:
    - "frame recurrent video inpainting by robust optical flow inference"
    - "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"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-5 (induced by source paper "an internal learning approach to video inpainting"):
  with members:
    - "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"
    - "characterizing and improving stability in neural style transfer"

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)