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: "free form video inpainting with 3d gated convolution and temporal patchgan"
  Abstract: Free-form video inpainting is a very challenging task that could be widely used for video editing such as text removal. Existing patch-based methods could not handle non-repetitive structures such as faces, while directly applying image-based inpainting models to videos will result in temporal inconsistency (see this http URL ). In this paper, we introduce a deep learn-ing based free-form video inpainting model, with proposed 3D gated convolutions to tackle the uncertainty of free-form masks and a novel Temporal PatchGAN loss to enhance temporal consistency. In addition, we collect videos and design a free-form mask generation algorithm to build the free-form video inpainting (FVI) dataset for training and evaluation of video inpainting models. We demonstrate the benefits of these components and experiments on both the FaceForensics and our FVI dataset suggest that our method is superior to existing ones. Related source code, full-resolution result videos and the FVI dataset could be found on Github this https URL .

Connected through hyperedge HE-1 (induced by source paper "gated texture cnn for efficient and configurable image denoising"):
  with members:
    - "cfsnet toward a controllable feature space for image restoration"
    - "residual non local attention networks for image restoration"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "real image denoising with feature attention"
    - "multi level wavelet convolutional neural networks"
    - "residual dense network for image restoration"
    - "class aware fully convolutional gaussian and poisson denoising"

Connected through hyperedge HE-2 (induced by source paper "xcos an explainable cosine metric for face verification task"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "improving the interpretability of deep neural networks with knowledge distillation"
    - "ted teaching ai to explain its decisions"
    - "arcface additive angular margin loss for deep face recognition"
    - "towards interpretable face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-3 (induced by source paper "background matting"):
  with members:
    - "deep flow guided video inpainting"
    - "deep video inpainting"
    - "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-4 (induced by source paper "deep long audio inpainting"):
  with members:
    - "deep video inpainting"
    - "adversarial generation of time frequency features with application in audio synthesis"
    - "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-5 (induced by source paper "learnable gated temporal shift module for deep video inpainting"):
  with members:
    - "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"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-6 (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"
    - "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"

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)