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: "hand held video deblurring via efficient fourier aggregation"
  Abstract: Videos captured with hand-held cameras often suffer from a significant amount of blur, mainly caused by the inevitable natural tremor of the photographer's hand. In this work, we present an algorithm that removes blur due to camera shake by combining information in the Fourier domain from nearby frames in a video. The dynamic nature of typical videos with the presence of multiple moving objects and occlusions makes this problem of camera shake removal extremely challenging, in particular when low complexity is needed. Given an input video frame, we first create a consistent registered version of temporally adjacent frames. Then, the set of consistently registered frames is block-wise fused in the Fourier domain with weights depending on the Fourier spectrum magnitude. The method is motivated from the physiological fact that camera shake blur has a random nature and therefore, nearby video frames are generally blurred differently. Experiments with numerous videos recorded in the wild, along with extensive comparisons, show that the proposed algorithm achieves state-of-the-art results while at the same time being much faster than its competitors.

Connected through hyperedge HE-1 (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"
    - "online video deblurring via dynamic temporal blending network"

Connected through hyperedge HE-2 (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"
    - "online video deblurring via dynamic temporal blending network"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "deep stacked hierarchical multi patch network for image deblurring"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "a deeper look at power normalizations"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "stereo video deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "generalized video deblurring for dynamic scenes"
    - "learning a convolutional neural network for non uniform motion blur removal"

Connected through hyperedge HE-4 (induced by source paper "handheld mobile photography in very low light"):
  with members:
    - "handheld multi frame super resolution"
    - "learning to see in the dark"
    - "unprocessing images for learned raw denoising"
    - "deep burst denoising"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "burst denoising with kernel prediction networks"
    - "fast fourier color constancy"
    - "convolutional color constancy"

Connected through hyperedge HE-5 (induced by source paper "the unreasonable effectiveness of deep features as a perceptual metric"):
  with members:
    - "nima neural image assessment"
    - "learned perceptual image enhancement"
    - "enhanced deep residual networks for single image super resolution"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "video frame interpolation via adaptive separable convolution"
    - "photographic image synthesis with cascaded refinement networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning representations for automatic colorization"

Connected through hyperedge HE-6 (induced by source paper "reblur2deblur deblurring videos via self supervised learning"):
  with members:
    - "learning blind motion deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "a neural approach to blind motion deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "deep video deblurring"
    - "from motion blur to motion flow a deep learning solution for removing heterogeneous motion blur"

Connected through hyperedge HE-7 (induced by source paper "learning blind motion deblurring"):
  with members:
    - "motion deblurring in the wild"
    - "cnn for license plate motion deblurring"
    - "end to end learning for image burst deblurring"
    - "a neural approach to blind motion deblurring"
    - "dynamic scene deblurring using a locally adaptive linear blur model"
    - "deep video deblurring"
    - "spatio temporal video autoencoder with differentiable memory"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "online video deblurring via dynamic temporal blending network"):
  with members:
    - "stereo video deblurring"
    - "identity mappings in deep residual networks"
    - "a neural approach to blind motion deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "dynamic scene deblurring using a locally adaptive linear blur model"
    - "deep video deblurring"
    - "dynamic filter networks"
    - "enhancenet single image super resolution through automated texture synthesis"

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)