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: "image inpainting for irregular holes using partial convolutions"
  Abstract: Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value). This often leads to artifacts such as color discrepancy and blurriness. Post-processing is usually used to reduce such artifacts, but are expensive and may fail. We propose the use of partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. We further include a mechanism to automatically generate an updated mask for the next layer as part of the forward pass. Our model outperforms other methods for irregular masks. We show qualitative and quantitative comparisons with other methods to validate our approach.

Connected through hyperedge HE-1 (induced by source paper "symmetric skip connection wasserstein gan for high resolution facial image inpainting"):
  with members:
    - "semi parametric image inpainting"
    - "shift net image inpainting via deep feature rearrangement"
    - "generative image inpainting with contextual attention"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep image prior"
    - "rethinking atrous convolution for semantic image segmentation"
    - "perceptual losses for real time style transfer and super resolution"
    - "high resolution image inpainting using multi scale neural patch synthesis"

Connected through hyperedge HE-2 (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"
    - "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"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "deep long audio inpainting"):
  with members:
    - "deep video inpainting"
    - "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"
    - "a context encoder for audio inpainting"
    - "efficient neural audio synthesis"
    - "speech commands a dataset for limited vocabulary speech recognition"

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"
    - "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-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"
    - "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 "vornet spatio temporally consistent video inpainting for object removal"):
  with members:
    - "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"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning of volume guided progressive view inpainting for 3d point scene completion from a single depth image"):
  with members:
    - "deep depth completion of a single rgb d image"
    - "two stream 3d semantic scene completion"
    - "free form image inpainting with gated convolution"
    - "view volume network for semantic scene completion from a single depth image"
    - "active object reconstruction using a guided view planner"
    - "adversarial semantic scene completion from a single depth image"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"

Connected through hyperedge HE-8 (induced by source paper "counterfactual depth from a single rgb image"):
  with members:
    - "deep depth completion of a single rgb d image"
    - "megadepth learning single view depth prediction from internet photos"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "unsupervised learning of depth and ego motion from video"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "surface normals in the wild"
    - "semi supervised deep learning for monocular depth map prediction"
    - "ai2 thor an interactive 3d environment for visual ai"

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)