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: "online video deblurring via dynamic temporal blending network"
  Abstract: State-of-the-art video deblurring methods are capable of removing non-uniform blur caused by unwanted camera shake and/or object motion in dynamic scenes. However, most existing methods are based on batch processing and thus need access to all recorded frames, rendering them computationally demanding and time consuming and thus limiting their practical use. In contrast, we propose an online (sequential) video deblurring method based on a spatio-temporal recurrent network that allows for real-time performance. In particular, we introduce a novel architecture which extends the receptive field while keeping the overall size of the network small to enable fast execution. In doing so, our network is able to remove even large blur caused by strong camera shake and/or fast moving objects. Furthermore, we propose a novel network layer that enforces temporal consistency between consecutive frames by dynamic temporal blending which compares and adaptively (at test time) shares features obtained at different time steps. We show the superiority of the proposed method in an extensive experimental evaluation.

Connected through hyperedge HE-1 (induced by source paper "blurry video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "feedback network for image super resolution"
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "adversarial spatio temporal learning for video deblurring"
    - "temporal cycle consistency learning"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "video enhancement with task oriented flow"

Connected through hyperedge HE-2 (induced by source paper "learning deep gradient descent optimization for image deconvolution"):
  with members:
    - "mptv matching pursuit based total variation minimization for image deconvolution"
    - "learning blind motion deblurring"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "learning aggregated transmission propagation networks for haze removal and beyond"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "learning to learn by gradient descent by gradient descent"
    - "learning fully convolutional networks for iterative non blind deconvolution"

Connected through hyperedge HE-3 (induced by source paper "self supervised linear motion deblurring"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "activestereonet end to end self supervised learning for active stereo systems"
    - "learning blind motion deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "david dual attentional video deblurring"):
  with members:
    - "segmentation aware image denoising without knowing true segmentation"
    - "learning to optimize in swarms"
    - "deblurgan v2 deblurring orders of magnitude faster and better"
    - "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"
    - "bridging the gap between computational photography and visual recognition"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "hierarchical attentive recurrent tracking"

Connected through hyperedge HE-5 (induced by source paper "spatio temporal filter adaptive network for video deblurring"):
  with members:
    - "davanet stereo deblurring with view aggregation"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "video frame interpolation via adaptive convolution"
    - "simultaneous stereo video deblurring and scene flow estimation"

Connected through hyperedge HE-6 (induced by source paper "deep video deblurring the devil is in the details"):
  with members:
    - "adversarial spatio temporal learning for video deblurring"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-7 (induced by source paper "davanet stereo deblurring with view aggregation"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "stereoscopic neural style transfer"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "pyramid stereo matching network"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "simultaneous stereo video deblurring and scene flow estimation"

Connected through hyperedge HE-8 (induced by source paper "joint stereo video deblurring scene flow estimation and moving object segmentation"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "learning a discriminative prior for blind image deblurring"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"

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)