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: "sceneflowfields dense interpolation of sparse scene flow correspondences"
  Abstract: While most scene flow methods use either variational optimization or a strong rigid motion assumption, we show for the first time that scene flow can also be estimated by dense interpolation of sparse matches. To this end, we find sparse matches across two stereo image pairs that are detected without any prior regularization and perform dense interpolation preserving geometric and motion boundaries by using edge information. A few iterations of variational energy minimization are performed to refine our results, which are thoroughly evaluated on the KITTI benchmark and additionally compared to state-of-the-art on MPI Sintel. For application in an automotive context, we further show that an optional ego-motion model helps to boost performance and blends smoothly into our approach to produce a segmentation of the scene into static and dynamic parts.

Connected through hyperedge HE-1 (induced by source paper "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation"):
  with members:
    - "pwoc 3d deep occlusion aware end to end scene flow estimation"
    - "deep rigid instance scene flow"
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "deep visual domain adaptation a survey"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "probflow joint optical flow and uncertainty estimation"
    - "cascaded scene flow prediction using semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "sense a shared encoder network for scene flow estimation"):
  with members:
    - "deep rigid instance scene flow"
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "segstereo exploiting semantic information for disparity estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "unified perceptual parsing for scene understanding"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "combining stereo disparity and optical flow for basic scene flow"

Connected through hyperedge HE-3 (induced by source paper "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"):
  with members:
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "understanding convolution for semantic segmentation"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "cnn based patch matching for optical flow with thresholded hinge embedding loss"

Connected through hyperedge HE-4 (induced by source paper "pwoc 3d deep occlusion aware end to end scene flow estimation"):
  with members:
    - "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "cascaded scene flow prediction using semantic segmentation"
    - "optical flow estimation using a spatial pyramid network"

Connected through hyperedge HE-5 (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"
    - "sceneednet a deep learning approach for scene flow estimation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "sfm net learning of structure and motion from video"
    - "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-6 (induced by source paper "lidar flow dense scene flow estimation from sparse lidar and stereo images"):
  with members:
    - "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation"
    - "delio decoupled lidar odometry"
    - "pointflownet learning representations for rigid motion estimation from point clouds"
    - "flownet3d learning scene flow in 3d point clouds"
    - "hallucinating dense optical flow from sparse lidar for autonomous vehicles"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

Connected through hyperedge HE-7 (induced by source paper "an empirical evaluation study on the training of sdc features for dense pixel matching"):
  with members:
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "facenet a unified embedding for face recognition and clustering"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-8 (induced by source paper "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"):
  with members:
    - "combining stereo disparity and optical flow for basic scene flow"
    - "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"
    - "accurate optical flow via direct cost volume processing"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "joint spatio temporal boundary detection and boundary flow prediction with a fully convolutional siamese network"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "segflow joint learning for video object segmentation and optical flow"
    - "higher order minimum cost lifted multicuts for motion segmentation"

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)