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: "learning for video super resolution through hr optical flow estimation"
  Abstract: Video super-resolution (SR) aims to generate a sequence of high-resolution (HR) frames with plausible and temporally consistent details from their low-resolution (LR) counterparts. The generation of accurate correspondence plays a significant role in video SR. It is demonstrated by traditional video SR methods that simultaneous SR of both images and optical flows can provide accurate correspondences and better SR results. However, LR optical flows are used in existing deep learning based methods for correspondence generation. In this paper, we propose an end-to-end trainable video SR framework to super-resolve both images and optical flows. Specifically, we first propose an optical flow reconstruction network (OFRnet) to infer HR optical flows in a coarse-to-fine manner. Then, motion compensation is performed according to the HR optical flows. Finally, compensated LR inputs are fed to a super-resolution network (SRnet) to generate the SR results. Extensive experiments demonstrate that HR optical flows provide more accurate correspondences than their LR counterparts and improve both accuracy and consistency performance. Comparative results on the Vid4 and DAVIS-10 datasets show that our framework achieves the state-of-the-art performance.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "deep video super resolution using hr optical flow estimation"):
  with members:
    - "frame recurrent video super resolution"
    - "scale recurrent network for deep image deblurring"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pyramid stereo matching network"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

Connected through hyperedge HE-3 (induced by source paper "learning parallax attention for stereo image super resolution"):
  with members:
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "fast and accurate single image super resolution via information distillation network"
    - "non local recurrent network for image restoration"
    - "multi task learning as multi objective optimization"
    - "residual dense network for image super resolution"
    - "left right comparative recurrent model for stereo matching"
    - "deep back projection networks for super resolution"
    - "dual attention network for scene segmentation"

Connected through hyperedge HE-4 (induced by source paper "fisr deep joint frame interpolation and super resolution with a multi scale temporal loss"):
  with members:
    - "fast spatio temporal residual network for video super resolution"
    - "depth aware video frame interpolation"
    - "image super resolution by neural texture transfer"
    - "recurrent back projection network for video super resolution"
    - "context aware synthesis for video frame interpolation"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-5 (induced by source paper "flickr1024 a large scale dataset for stereo image super resolution"):
  with members:
    - "video enhancement with task oriented flow"
    - "no reference color image quality assessment from entropy to perceptual quality"
    - "learning for disparity estimation through feature constancy"
    - "learning a no reference quality metric for single image super resolution"

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)