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: "video enhancement with task oriented flow"
  Abstract: Many video enhancement algorithms rely on optical flow to register frames in a video sequence. Precise flow estimation is however intractable; and optical flow itself is often a sub-optimal representation for particular video processing tasks. In this paper, we propose task-oriented flow (TOFlow), a motion representation learned in a self-supervised, task-specific manner. We design a neural network with a trainable motion estimation component and a video processing component, and train them jointly to learn the task-oriented flow. For evaluation, we build Vimeo-90K, a large-scale, high-quality video dataset for low-level video processing. TOFlow outperforms traditional optical flow on standard benchmarks as well as our Vimeo-90K dataset in three video processing tasks: frame interpolation, video denoising/deblocking, and video super-resolution.

Connected through hyperedge HE-1 (induced by source paper "softmax splatting for video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "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"
    - "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 to video synthesis"

Connected through hyperedge HE-3 (induced by source paper "deep blind video super resolution"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "blind super resolution kernel estimation using an internal gan"
    - "generative adversarial networks and perceptual losses for video super resolution"
    - "recurrent back projection network for video super resolution"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "neural blind deconvolution using deep priors"
    - "frame recurrent video super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"

Connected through hyperedge HE-4 (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"
    - "depth aware video frame interpolation"
    - "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-5 (induced by source paper "end to end trainable video super resolution based on a new mechanism for implicit motion estimation and compensation"):
  with members:
    - "residual dense network for image super resolution"
    - "detail revealing deep video super resolution"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "dynamic filter networks"
    - "real time video super resolution with spatio temporal networks and motion compensation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "unsupervised bi directional flow based video generation from one snapshot"):
  with members:
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "flow grounded spatial temporal video prediction from still images"
    - "dual motion gan for future flow embedded video prediction"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "mocogan decomposing motion and content for video generation"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "unsupervised learning of long term motion dynamics for videos"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-7 (induced by source paper "depth aware video frame interpolation"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement"
    - "context aware synthesis for video frame interpolation"
    - "megadepth learning single view depth prediction from internet photos"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "video frame interpolation via adaptive convolution"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-8 (induced by source paper "edvr video restoration with enhanced deformable convolutional networks"):
  with members:
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "adversarial spatio temporal learning for video deblurring"
    - "path restore learning network path selection for image restoration"
    - "frame recurrent video super resolution"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "object detection in video with spatiotemporal sampling networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "scale recurrent network for deep image deblurring"

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)