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: "sceneednet a deep learning approach for scene flow estimation"
  Abstract: Estimating scene flow in RGB-D videos is attracting much interest of the computer vision researchers, due to its potential applications in robotics. The state-of-the-art techniques for scene flow estimation typically rely on the knowledge of scene structure of the frame and the correspondence between frames. However, with the availability of large RGB-D data captured from depth sensors, learning representations for estimation of scene flow has become possible. This paper introduces a first effort to apply a deep learning method for direct estimation of scene flow by presenting a fully convolutional neural network with an encoder-decoder (ED) architecture. The proposed network SceneEDNet involves estimation of three dimensional motion vectors of all the scene points from sequence of stereo images. The training for direct estimation of scene flow is done using consecutive pairs of stereo images and corresponding scene flow ground truth.

Connected through hyperedge HE-1 (induced by source paper "a conditional adversarial network for scene flow estimation"):
  with members:
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "sfm net learning of structure and motion from video"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-2 (induced by source paper "automated scene flow data generation for training and verification"):
  with members:
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "playing for benchmarks"
    - "cascaded scene flow prediction using semantic segmentation"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "a continuous optimization approach for efficient and accurate scene flow"
    - "virtual worlds as proxy for multi object tracking analysis"

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)