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: "combining stereo disparity and optical flow for basic scene flow"
  Abstract: Scene flow is a description of real world motion in 3D that contains more information than optical flow. Because of its complexity there exists no applicable variant for real-time scene flow estimation in an automotive or commercial vehicle context that is sufficiently robust and accurate. Therefore, many applications estimate the 2D optical flow instead. In this paper, we examine the combination of top-performing state-of-the-art optical flow and stereo disparity algorithms in order to achieve a basic scene flow. On the public KITTI Scene Flow Benchmark we demonstrate the reasonable accuracy of the combination approach and show its speed in computation.

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"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "probflow joint optical flow and uncertainty estimation"
    - "cascaded scene flow prediction using semantic segmentation"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"

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"
    - "hierarchical discrete distribution decomposition for match density estimation"

Connected through hyperedge HE-3 (induced by source paper "driving datasets literature review"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "object detection in 20 years a survey"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the streetlearn environment and dataset"

Connected through hyperedge HE-4 (induced by source paper "tw smnet deep multitask learning of tele wide stereo matching"):
  with members:
    - "amnet deep atrous multiscale stereo disparity estimation networks"
    - "deep ordinal regression network for monocular depth estimation"
    - "pyramid stereo matching network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "label refinement network for coarse to fine semantic segmentation"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "single image depth estimation by dilated deep residual convolutional neural network and soft weight sum inference"

Connected through hyperedge HE-5 (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"
    - "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"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"
    - "cnn based patch matching for optical flow with thresholded hinge embedding loss"

Connected through hyperedge HE-6 (induced by source paper "delio decoupled lidar odometry"):
  with members:
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "imls slam scan to model matching based on 3d data"
    - "limo lidar monocular visual odometry"
    - "segmatch segment based place recognition in 3d point clouds"
    - "cnn for imu assisted odometry estimation using velodyne lidar"
    - "a review of mean shift algorithms for clustering"
    - "approximate k nearest neighbour based spatial clustering using k d tree"

Connected through hyperedge HE-7 (induced by source paper "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"):
  with members:
    - "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"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"

Connected through hyperedge HE-8 (induced by source paper "sceneednet a deep learning approach for scene flow estimation"):
  with members:
    - "end to end recovery of human shape and pose"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "scene flow estimation a survey"
    - "shapenet an information rich 3d model repository"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "flownet learning optical flow with convolutional 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)