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: "parametric object motion from blur"
  Abstract: Motion blur can adversely affect a number of vision tasks, hence it is generally considered a nuisance. We instead treat motion blur as a useful signal that allows to compute the motion of objects from a single image. Drawing on the success of joint segmentation and parametric motion models in the context of optical flow estimation, we propose a parametric object motion model combined with a segmentation mask to exploit localized, non-uniform motion blur. Our parametric image formation model is differentiable w.r.t. the motion parameters, which enables us to generalize marginal-likelihood techniques from uniform blind deblurring to localized, non-uniform blur. A two-stage pipeline, first in derivative space and then in image space, allows to estimate both parametric object motion as well as a motion segmentation from a single image alone. Our experiments demonstrate its ability to cope with very challenging cases of object motion blur.

Connected through hyperedge HE-1 (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-2 (induced by source paper "self supervised blur detection from synthetically blurred scenes"):
  with members:
    - "single image depth estimation trained via depth from defocus cues"
    - "learning to adapt structured output space for semantic segmentation"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "self supervised feature learning by learning to spot artifacts"
    - "spatially varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "a unified approach of multi scale deep and hand crafted features for defocus estimation"

Connected through hyperedge HE-3 (induced by source paper "effects of image degradations to cnn based image classification"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "real time detection tracking and classification of moving and stationary objects using multiple fisheye images"
    - "squeeze and excitation networks"
    - "dehazenet an end to end system for single image haze removal"
    - "how image degradations affect deep cnn based face recognition"
    - "studying very low resolution recognition using deep networks"
    - "the next best underwater view"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "motion deblurring in the wild"):
  with members:
    - "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"
    - "flownet learning optical flow with convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "generalized video deblurring for dynamic scenes"
    - "learning a convolutional neural network for non uniform motion blur removal"

Connected through hyperedge HE-5 (induced by source paper "probflow joint optical flow and uncertainty estimation"):
  with members:
    - "full flow optical flow estimation by global optimization over regular grids"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "the fast bilateral solver"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

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)