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: "recurrent back projection network for video super resolution"
  Abstract: We proposed a novel architecture for the problem of video super-resolution. We integrate spatial and temporal contexts from continuous video frames using a recurrent encoder-decoder module, that fuses multi-frame information with the more traditional, single frame super-resolution path for the target frame. In contrast to most prior work where frames are pooled together by stacking or warping, our model, the Recurrent Back-Projection Network (RBPN) treats each context frame as a separate source of information. These sources are combined in an iterative refinement framework inspired by the idea of back-projection in multiple-image super-resolution. This is aided by explicitly representing estimated inter-frame motion with respect to the target, rather than explicitly aligning frames. We propose a new video super-resolution benchmark, allowing evaluation at a larger scale and considering videos in different motion regimes. Experimental results demonstrate that our RBPN is superior to existing methods on several datasets.

Connected through hyperedge HE-1 (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"
    - "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"
    - "to learn image super resolution use a gan to learn how to do image degradation first"

Connected through hyperedge HE-2 (induced by source paper "zooming slow mo fast and accurate one stage space time video super resolution"):
  with members:
    - "depth aware video frame interpolation"
    - "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"
    - "context aware synthesis for video frame interpolation"

Connected through hyperedge HE-3 (induced by source paper "video face super resolution with motion adaptive feedback cell"):
  with members:
    - "fast spatio temporal residual 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"
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"

Connected through hyperedge HE-4 (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"
    - "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"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "learning to super resolve intensity images from events"):
  with members:
    - "high speed and high dynamic range video with an event camera"
    - "end to end learning of representations for asynchronous event based data"
    - "frame recurrent video super resolution"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "continuous time intensity estimation using event cameras"
    - "deep back projection networks for super resolution"

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"
    - "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"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

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)