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: "detail revealing deep video super resolution"
  Abstract: Previous CNN-based video super-resolution approaches need to align multiple frames to the reference. In this paper, we show that proper frame alignment and motion compensation is crucial for achieving high quality results. We accordingly propose a `sub-pixel motion compensation' (SPMC) layer in a CNN framework. Analysis and experiments show the suitability of this layer in video SR. The final end-to-end, scalable CNN framework effectively incorporates the SPMC layer and fuses multiple frames to reveal image details. Our implementation can generate visually and quantitatively high-quality results, superior to current state-of-the-arts, without the need of parameter tuning.

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"
    - "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-2 (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"
    - "video enhancement with task oriented flow"
    - "neural blind deconvolution using deep priors"
    - "frame recurrent video super resolution"

Connected through hyperedge HE-3 (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-4 (induced by source paper "learned multi view texture super resolution"):
  with members:
    - "3d appearance super resolution with deep learning"
    - "deep learning for single image super resolution a brief review"
    - "fast and accurate single image super resolution via information distillation network"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimedia super resolution via deep learning a survey"
    - "a fully progressive approach to single image super resolution"
    - "residual dense network for image super resolution"
    - "deep network interpolation for continuous imagery effect transition"

Connected through hyperedge HE-5 (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-6 (induced by source paper "video face super resolution with motion adaptive feedback cell"):
  with members:
    - "fast spatio temporal residual network for video super resolution"
    - "recurrent back projection network for video super resolution"
    - "frame recurrent video super resolution"
    - "category based deep cca for fine grained venue discovery from multimodal data"
    - "cosface large margin cosine loss for deep face recognition"
    - "super identity convolutional neural network for face hallucination"
    - "residual dense network for image super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

Connected through hyperedge HE-7 (induced by source paper "vesr net the winning solution to youku video enhancement and super resolution challenge"):
  with members:
    - "fast spatio temporal residual network for video super resolution"
    - "recurrent back projection network for video super resolution"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "frame recurrent video super resolution"
    - "3dsrnet video super resolution using 3d convolutional neural networks"
    - "image super resolution using very deep residual channel attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "non local neural networks"

Connected through hyperedge HE-8 (induced by source paper "is there tradeoff between spatial and temporal in video super resolution"):
  with members:
    - "two stream action recognition oriented video super resolution"
    - "on the classification distortion perception tradeoff"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "learning blind video temporal consistency"
    - "the perception distortion tradeoff"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "temporal segment networks towards good practices for deep action 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)