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: "a temporally aware interpolation network for video frame inpainting"
  Abstract: We propose the first deep learning solution to video frame inpainting, a challenging instance of the general video inpainting problem with applications in video editing, manipulation, and forensics. Our task is less ambiguous than frame interpolation and video prediction because we have access to both the temporal context and a partial glimpse of the future, allowing us to better evaluate the quality of a model's predictions objectively. We devise a pipeline composed of two modules: a bidirectional video prediction module, and a temporally-aware frame interpolation module. The prediction module makes two intermediate predictions of the missing frames, one conditioned on the preceding frames and the other conditioned on the following frames, using a shared convolutional LSTM-based encoder-decoder. The interpolation module blends the intermediate predictions to form the final result. Specifically, it utilizes time information and hidden activations from the video prediction module to resolve disagreements between the predictions. Our experiments demonstrate that our approach produces more accurate and qualitatively satisfying results than a state-of-the-art video prediction method and many strong frame inpainting baselines.

Connected through hyperedge HE-1 (induced by source paper "hypercon image to video model transfer for video to video translation tasks"):
  with members:
    - "deep flow guided video inpainting"
    - "deep video inpainting"
    - "copy and paste networks for deep video inpainting"
    - "video to video synthesis"
    - "learning blind video temporal consistency"
    - "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-2 (induced by source paper "the angel is in the priors improving gan based image and sequence inpainting with better noise and structural priors"):
  with members:
    - "generative image inpainting with contextual attention"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "learning from simulated and unsupervised images through adversarial training"
    - "semantic image inpainting with deep generative models"
    - "context encoders feature learning by inpainting"
    - "real time video super resolution with spatio temporal networks and motion compensation"

Connected through hyperedge HE-3 (induced by source paper "multi view frame reconstruction with conditional gan"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "long term video interpolation with bidirectional predictive network"
    - "video frame interpolation via adaptive separable convolution"
    - "frame interpolation with multi scale deep loss functions and generative adversarial networks"
    - "video frame synthesis using deep voxel flow"
    - "generative adversarial text to image synthesis"
    - "deep predictive coding networks for video prediction and unsupervised learning"

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)