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: "super slomo high quality estimation of multiple intermediate frames for video interpolation"
  Abstract: Given two consecutive frames, video interpolation aims at generating intermediate frame(s) to form both spatially and temporally coherent video sequences. While most existing methods focus on single-frame interpolation, we propose an end-to-end convolutional neural network for variable-length multi-frame video interpolation, where the motion interpretation and occlusion reasoning are jointly modeled. We start by computing bi-directional optical flow between the input images using a U-Net architecture. These flows are then linearly combined at each time step to approximate the intermediate bi-directional optical flows. These approximate flows, however, only work well in locally smooth regions and produce artifacts around motion boundaries. To address this shortcoming, we employ another U-Net to refine the approximated flow and also predict soft visibility maps. Finally, the two input images are warped and linearly fused to form each intermediate frame. By applying the visibility maps to the warped images before fusion, we exclude the contribution of occluded pixels to the interpolated intermediate frame to avoid artifacts. Since none of our learned network parameters are time-dependent, our approach is able to produce as many intermediate frames as needed. We use 1,132 video clips with 240-fps, containing 300K individual video frames, to train our network. Experimental results on several datasets, predicting different numbers of interpolated frames, demonstrate that our approach performs consistently better than existing methods.

Connected through hyperedge HE-1 (induced by source paper "stochastic latent residual video prediction"):
  with members:
    - "videoflow a conditional flow based model for stochastic video generation"
    - "improved conditional vrnns for video prediction"
    - "cubic lstms for video prediction"
    - "unsupervised learning of object structure and dynamics from videos"
    - "scaling autoregressive video models"
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-2 (induced by source paper "softmax splatting for video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "painting many pasts synthesizing time lapse videos of paintings"):
  with members:
    - "lpaintb learning to paint from self supervision"
    - "visual deprojection probabilistic recovery of collapsed dimensions"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "context aware synthesis for video frame interpolation"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "learning to generate long term future via hierarchical prediction"
    - "latent constraints learning to generate conditionally from unconditional generative models"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "blurry video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "adacof adaptive collaboration of flows for video frame interpolation"):
  with members:
    - "depth aware video frame interpolation"
    - "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"
    - "unsupervised learning of disentangled representations from video"
    - "video frame interpolation via adaptive convolution"
    - "deformable convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "zooming slow mo fast and accurate one stage space time video super resolution"):
  with members:
    - "depth aware video frame interpolation"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "joint wasserstein distribution matching"):
  with members:
    - "unsupervised video to video translation"
    - "dual reconstruction nets for image super resolution with gradient sensitive loss"
    - "improving shape deformation in unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "unsupervised attention guided image to image translation"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "deep view morphing"

Connected through hyperedge HE-8 (induced by source paper "deep slow motion video reconstruction with hybrid imaging system"):
  with members:
    - "depth aware video frame interpolation"
    - "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"
    - "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"

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)