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: "frame interpolation with multi scale deep loss functions and generative adversarial networks"
  Abstract: Frame interpolation attempts to synthesise intermediate frames given one or more consecutive video frames. In recent years, deep learning approaches, and in particular convolutional neural networks, have succeeded at tackling low- and high-level computer vision problems including frame interpolation. There are two main pursuits in this line of research, namely algorithm efficiency and reconstruction quality. In this paper, we present a multi-scale generative adversarial network for frame interpolation (FIGAN). To maximise the efficiency of our network, we propose a novel multi-scale residual estimation module where the predicted flow and synthesised frame are constructed in a coarse-to-fine fashion. To improve the quality of synthesised intermediate video frames, our network is jointly supervised at different levels with a perceptual loss function that consists of an adversarial and two content losses. We evaluate the proposed approach using a collection of 60fps videos from YouTube-8m. Our results improve the state-of-the-art accuracy and efficiency, and a subjective visual quality comparable to the best performing interpolation method.

Connected through hyperedge HE-1 (induced by source paper "learning occlusion aware view synthesis for light fields"):
  with members:
    - "a system for acquiring processing and rendering panoramic light field stills for virtual reality"
    - "pushing the boundaries of view extrapolation with multiplane images"
    - "context aware synthesis for video frame interpolation"
    - "stereo magnification learning view synthesis using multiplane images"
    - "pyramid stereo matching network"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "end to end learning of geometry and context for deep stereo regression"
    - "residual conv deconv grid network for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement"):
  with members:
    - "video enhancement with task oriented flow"
    - "context aware synthesis for video frame interpolation"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "accurate optical flow via direct cost volume processing"
    - "video frame interpolation via adaptive convolution"
    - "enhanced deep residual networks for single image super resolution"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-3 (induced by source paper "multi view frame reconstruction with conditional gan"):
  with members:
    - "a temporally aware interpolation network for video frame inpainting"
    - "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"
    - "video frame synthesis using deep voxel flow"
    - "generative adversarial text to image synthesis"
    - "deep predictive coding networks for video prediction and unsupervised learning"

Connected through hyperedge HE-4 (induced by source paper "context adaptive neural network based prediction for image compression"):
  with members:
    - "predicting deeper into the future of semantic segmentation"
    - "video frame interpolation via adaptive convolution"
    - "enhanced deep residual networks for single image super resolution"
    - "context encoders feature learning by inpainting"
    - "pixel recurrent neural networks"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "implementing adaptive separable convolution for video frame interpolation"):
  with members:
    - "context aware synthesis for video frame interpolation"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "video frame interpolation via adaptive convolution"
    - "the 2017 davis challenge on video object segmentation"
    - "video frame interpolation via adaptive separable convolution"
    - "deep residual learning for image recognition"

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)