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: "robust optical flow estimation of double layer images under transparency or reflection"
  Abstract: This paper deals with a challenging, frequently encountered, yet not properly investigated problem in two-frame optical flow estimation. That is, the input frames are compounds of two imaging layers -- one desired background layer of the scene, and one distracting, possibly moving layer due to transparency or reflection. In this situation, the conventional brightness constancy constraint -- the cornerstone of most existing optical flow methods -- will no longer be valid. In this paper, we propose a robust solution to this problem. The proposed method performs both optical flow estimation, and image layer separation. It exploits a generalized double-layer brightness consistency constraint connecting these two tasks, and utilizes the priors for both of them. Experiments on both synthetic data and real images have confirmed the efficacy of the proposed method. To the best of our knowledge, this is the first attempt towards handling generic optical flow fields of two-frame images containing transparency or reflection.

Connected through hyperedge HE-1 (induced by source paper "single image reflection removal exploiting misaligned training data and network enhancements"):
  with members:
    - "generative single image reflection separation"
    - "group normalization"
    - "the relativistic discriminator a key element missing from standard gan"
    - "single image reflection removal using deep encoder decoder network"
    - "single image reflection separation with perceptual losses"
    - "the contextual loss for image transformation with non aligned data"
    - "crrn multi scale guided concurrent reflection removal network"
    - "context encoding for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep reflection prior"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "fast single image reflection suppression via convex optimization"
    - "single image reflection removal exploiting misaligned training data and network enhancements"
    - "single image reflection separation with perceptual losses"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "crrn multi scale guided concurrent reflection removal network"
    - "reflection separation using guided annotation"

Connected through hyperedge HE-3 (induced by source paper "semantic guided single image reflection removal"):
  with members:
    - "single image reflection separation with perceptual losses"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "crrn multi scale guided concurrent reflection removal network"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "rethinking atrous convolution for semantic image segmentation"
    - "separating reflection and transmission images in the wild"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "stereo computation for a single mixture image"):
  with members:
    - "self supervised learning for stereo matching with self improving ability"
    - "unsupervised learning of depth and ego motion from video"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "image to image translation with conditional adversarial networks"
    - "colorful image colorization"

Connected through hyperedge HE-5 (induced by source paper "a generic deep architecture for single image reflection removal and image smoothing"):
  with members:
    - "holistically nested edge detection"
    - "accurate image super resolution using very deep convolutional networks"
    - "on vectorization of deep convolutional neural networks for vision tasks"
    - "deep residual learning for image recognition"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "deeply recursive convolutional network for image super resolution"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "depth map completion by jointly exploiting blurry color images and sparse depth maps"):
  with members:
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "stereo video deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "a deep primal dual network for guided depth super resolution"
    - "from motion blur to motion flow a deep learning solution for removing heterogeneous motion blur"
    - "generalized video deblurring for dynamic scenes"
    - "very deep convolutional networks for large scale image recognition"

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)