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: "tdan temporally deformable alignment network for video super resolution"
  Abstract: Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple neighboring frames (supporting frames). Due to varying motion of cameras or objects, the reference frame and each support frame are not aligned. Therefore, temporal alignment is a challenging yet important problem for VSR. Previous VSR methods usually utilize optical flow between the reference frame and each supporting frame to wrap the supporting frame for temporal alignment. Therefore, the performance of these image-level wrapping-based models will highly depend on the prediction accuracy of optical flow, and inaccurate optical flow will lead to artifacts in the wrapped supporting frames, which also will be propagated into the reconstructed HR video frame. To overcome the limitation, in this paper, we propose a temporal deformable alignment network (TDAN) to adaptively align the reference frame and each supporting frame at the feature level without computing optical flow. The TDAN uses features from both the reference frame and each supporting frame to dynamically predict offsets of sampling convolution kernels. By using the corresponding kernels, TDAN transforms supporting frames to align with the reference frame. To predict the HR video frame, a reconstruction network taking aligned frames and the reference frame is utilized. Experimental results demonstrate the effectiveness of the proposed TDAN-based VSR model.

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"
    - "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-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"
    - "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-3 (induced by source paper "video saliency prediction using enhanced spatiotemporal alignment network"):
  with members:
    - "reppoints point set representation for object detection"
    - "simple vs complex temporal recurrences for video saliency prediction"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (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"
    - "video enhancement with task oriented flow"
    - "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"

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)