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: "depth aware video frame interpolation"
  Abstract: Video frame interpolation aims to synthesize nonexistent frames in-between the original frames. While significant advances have been made from the recent deep convolutional neural networks, the quality of interpolation is often reduced due to large object motion or occlusion. In this work, we propose a video frame interpolation method which explicitly detects the occlusion by exploring the depth information. Specifically, we develop a depth-aware flow projection layer to synthesize intermediate flows that preferably sample closer objects than farther ones. In addition, we learn hierarchical features to gather contextual information from neighboring pixels. The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame. Our model is compact, efficient, and fully differentiable. Quantitative and qualitative results demonstrate that the proposed model performs favorably against state-of-the-art frame interpolation methods on a wide variety of datasets.

Connected through hyperedge HE-1 (induced by source paper "softmax splatting for video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "video enhancement with task oriented flow"
    - "phasenet for video frame interpolation"
    - "temporal interpolation as an unsupervised pretraining task for optical flow estimation"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning to synthesize motion blur"
    - "deep video color propagation"
    - "geometry aware deep network for single image novel view synthesis"

Connected through hyperedge HE-2 (induced by source paper "blurry video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "feedback network for image super resolution"
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "adversarial spatio temporal learning for video deblurring"
    - "temporal cycle consistency learning"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "video enhancement with task oriented flow"
    - "video to video synthesis"

Connected through hyperedge HE-3 (induced by source paper "adacof adaptive collaboration of flows for video frame interpolation"):
  with members:
    - "phasenet for video frame interpolation"
    - "the perception distortion tradeoff"
    - "memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement"
    - "context aware synthesis for video frame interpolation"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "unsupervised learning of disentangled representations from video"
    - "video frame interpolation via adaptive convolution"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "zooming slow mo fast and accurate one stage space time video super resolution"):
  with members:
    - "recurrent back projection network for video super resolution"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "frame recurrent video super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "learning for video super resolution through hr optical flow estimation"
    - "memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement"
    - "deformable convnets v2 more deformable better results"
    - "context aware synthesis for video frame interpolation"

Connected through hyperedge HE-5 (induced by source paper "deep slow motion video reconstruction with hybrid imaging system"):
  with members:
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "phasenet for video frame interpolation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "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"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "video frame interpolation via adaptive separable convolution"

Connected through hyperedge HE-6 (induced by source paper "across scales across dimensions temporal super resolution using deep internal learning"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "video enhancement with task oriented flow"
    - "phasenet for video frame interpolation"
    - "ingan capturing and remapping the dna of a natural image"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "understanding the effective receptive field in deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "temporal interpolation of geostationary satellite imagery with task specific optical flow"):
  with members:
    - "super resolution of sentinel 2 images learning a globally applicable deep neural network"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "deepsat a learning framework for satellite imagery"

Connected through hyperedge HE-8 (induced by source paper "gradient boosting neural networks grownet"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "learning data augmentation strategies for object detection"
    - "catboost unbiased boosting with categorical features"
    - "xgboost a scalable tree boosting system"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "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)