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: "end to end learning of video super resolution with motion compensation"
  Abstract: Learning approaches have shown great success in the task of super-resolving an image given a low resolution input. Video super-resolution aims for exploiting additionally the information from multiple images. Typically, the images are related via optical flow and consecutive image warping. In this paper, we provide an end-to-end video super-resolution network that, in contrast to previous works, includes the estimation of optical flow in the overall network architecture. We analyze the usage of optical flow for video super-resolution and find that common off-the-shelf image warping does not allow video super-resolution to benefit much from optical flow. We rather propose an operation for motion compensation that performs warping from low to high resolution directly. We show that with this network configuration, video super-resolution can benefit from optical flow and we obtain state-of-the-art results on the popular test sets. We also show that the processing of whole images rather than independent patches is responsible for a large increase in accuracy.

Connected through hyperedge HE-1 (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"
    - "learning for video super resolution through hr optical flow estimation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "self enhanced convolutional network for facial video hallucination"):
  with members:
    - "frame recurrent video super resolution"
    - "the relativistic discriminator a key element missing from standard gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "residual dense network for image super resolution"
    - "efficient super resolution for large scale images using attentional gan"
    - "face super resolution through wasserstein gans"
    - "non local neural networks"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

Connected through hyperedge HE-3 (induced by source paper "video enhancement with task oriented flow"):
  with members:
    - "frame recurrent video super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "multi frame quality enhancement for compressed video"
    - "context aware synthesis for video frame interpolation"
    - "flow guided feature aggregation for video object detection"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "accurate optical flow via direct cost volume processing"

Connected through hyperedge HE-4 (induced by source paper "a single video super resolution gan for multiple downsampling operators based on pseudo inverse image formation models"):
  with members:
    - "generative adversarial networks and perceptual losses for video super resolution"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "learning a single convolutional super resolution network for multiple degradations"
    - "learning deep cnn denoiser prior for image restoration"
    - "detail revealing deep video super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "mfqe 2 0 a new approach for multi frame quality enhancement on compressed video"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "multi frame quality enhancement for compressed video"
    - "saliency guided complexity control for hevc decoding"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "detail revealing deep video super resolution"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-6 (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"
    - "learning for video super resolution through hr optical flow estimation"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "generative adversarial networks and perceptual losses for video super resolution"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "enhanced deep residual networks for single image super resolution"
    - "learning deep cnn denoiser prior for image restoration"
    - "detail revealing deep video super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "real time video super resolution with spatio temporal networks and motion compensation"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "perceptual video super resolution with enhanced temporal consistency"):
  with members:
    - "frame recurrent video super resolution"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "characterizing and improving stability in neural style transfer"
    - "perceptual losses for real time style transfer and super resolution"
    - "optical flow estimation using a spatial pyramid network"

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)