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: "learning to synthesize motion blur"
  Abstract: We present a technique for synthesizing a motion blurred image from a pair of unblurred images captured in succession. To build this system we motivate and design a differentiable "line prediction" layer to be used as part of a neural network architecture, with which we can learn a system to regress from image pairs to motion blurred images that span the capture time of the input image pair. Training this model requires an abundance of data, and so we design and execute a strategy for using frame interpolation techniques to generate a large-scale synthetic dataset of motion blurred images and their respective inputs. We additionally capture a high quality test set of real motion blurred images, synthesized from slow motion videos, with which we evaluate our model against several baseline techniques that can be used to synthesize motion blur. Our model produces higher accuracy output than our baselines, and is significantly faster than baselines with competitive accuracy.

Connected through hyperedge HE-1 (induced by source paper "softmax splatting for video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "video enhancement with task oriented flow"
    - "phasenet for video frame interpolation"
    - "temporal interpolation as an unsupervised pretraining task for optical flow estimation"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep video color propagation"
    - "geometry aware deep network for single image novel view synthesis"

Connected through hyperedge HE-2 (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-3 (induced by source paper "abba saliency regularized motion based adversarial blur attack"):
  with members:
    - "effects of blur and deblurring to visual object tracking"
    - "brain inspired reverse adversarial examples"
    - "spatial aware online adversarial perturbations against visual object tracking"
    - "a simple pooling based design for real time salient object detection"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "amora black box adversarial morphing attack"
    - "adversarial attacks and defences competition"
    - "adef an iterative algorithm to construct adversarial deformations"

Connected through hyperedge HE-4 (induced by source paper "image restoration for under display camera"):
  with members:
    - "when awgn based denoiser meets real noises"
    - "dense haze a benchmark for image dehazing with dense haze and haze free images"
    - "noise flow noise modeling with conditional normalizing flows"
    - "learning raw image denoising with bayer pattern unification and bayer preserving augmentation"
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "esrgan enhanced super resolution generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "quadratic video interpolation"):
  with members:
    - "learning deformable kernels for image and video denoising"
    - "memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement"
    - "context aware synthesis for video frame interpolation"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "video frame interpolation via adaptive convolution"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "video frame interpolation via adaptive separable convolution"

Connected through hyperedge HE-6 (induced by source paper "deepblindness fast blindness map estimation and blindness type classification for outdoor scene from single color image"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "deep depth from defocus how can defocus blur improve 3d estimation using dense neural networks"
    - "haze density estimation via modeling of scattering coefficients of iso depth regions"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"
    - "spatially varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes"
    - "a unified approach of multi scale deep and hand crafted features for defocus estimation"
    - "sparsity invariant cnns"
    - "joint transmission map estimation and dehazing using deep 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)