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: "scene flow estimation a survey"
  Abstract: This paper is the first to review the scene flow estimation field, which analyzes and compares methods, technical challenges, evaluation methodologies and performance of scene flow estimation. Existing algorithms are categorized in terms of scene representation, data source, and calculation scheme, and the pros and cons in each category are compared briefly. The datasets and evaluation protocols are enumerated, and the performance of the most representative methods is presented. A future vision is illustrated with few questions arisen for discussion. This survey presents a general introduction and analysis of scene flow estimation.

Connected through hyperedge HE-1 (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-2 (induced by source paper "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"):
  with members:
    - "flownet3d learning scene flow in 3d point clouds"
    - "splatnet sparse lattice networks for point cloud processing"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-3 (induced by source paper "deep part induction from articulated object pairs"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "motion based object segmentation based on dense rgb d scene flow"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "multiframe scene flow with piecewise rigid motion"

Connected through hyperedge HE-4 (induced by source paper "shape2motion joint analysis of motion parts and attributes from 3d shapes"):
  with members:
    - "deep part induction from articulated object pairs"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "3d shape segmentation with projective convolutional networks"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "data driven shape analysis and processing"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-5 (induced by source paper "sceneednet a deep learning approach for scene flow estimation"):
  with members:
    - "combining stereo disparity and optical flow for basic scene flow"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "motion based object segmentation based on dense rgb d scene flow"):
  with members:
    - "interactive perception leveraging action in perception and perception in action"
    - "higher order minimum cost lifted multicuts for motion segmentation"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "shapenet an information rich 3d model repository"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "extracting contact and motion from manipulation videos"):
  with members:
    - "cilantro a lean versatile and efficient library for point cloud data processing"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "multiple object recognition with visual attention"
    - "two stream convolutional networks for action recognition in videos"
    - "a tutorial on spectral clustering"

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)