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: "pointflownet learning representations for rigid motion estimation from point clouds"
  Abstract: Despite significant progress in image-based 3D scene flow estimation, the performance of such approaches has not yet reached the fidelity required by many applications. Simultaneously, these applications are often not restricted to image-based estimation: laser scanners provide a popular alternative to traditional cameras, for example in the context of self-driving cars, as they directly yield a 3D point cloud. In this paper, we propose to estimate 3D motion from such unstructured point clouds using a deep neural network. In a single forward pass, our model jointly predicts 3D scene flow as well as the 3D bounding box and rigid body motion of objects in the scene. While the prospect of estimating 3D scene flow from unstructured point clouds is promising, it is also a challenging task. We show that the traditional global representation of rigid body motion prohibits inference by CNNs, and propose a translation equivariant representation to circumvent this problem. For training our deep network, a large dataset is required. Because of this, we augment real scans from KITTI with virtual objects, realistically modeling occlusions and simulating sensor noise. A thorough comparison with classic and learning-based techniques highlights the robustness of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "pointpwc net a coarse to fine network for supervised and self supervised scene flow estimation on 3d point clouds"):
  with members:
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "flownet3d learning scene flow in 3d point clouds"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "splatnet sparse lattice networks for point cloud processing"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "pointconv deep convolutional networks on 3d point clouds"

Connected through hyperedge HE-2 (induced by source paper "flownet3d geometric losses for deep scene flow estimation"):
  with members:
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "deep rigid instance scene flow"
    - "flownet3d learning scene flow in 3d point clouds"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "motion based object segmentation based on dense rgb d scene flow"
    - "pyramid stereo matching network"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"

Connected through hyperedge HE-3 (induced by source paper "live reconstruction of large scale dynamic outdoor worlds"):
  with members:
    - "fusion volumetric object level slam"
    - "open3d a modern library for 3d data processing"
    - "yolov3 an incremental improvement"
    - "robust dense mapping for large scale dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "pyramid stereo matching network"
    - "mid fusion octree based object level multi instance dynamic slam"
    - "joint 3d proposal generation and object detection from view aggregation"

Connected through hyperedge HE-4 (induced by source paper "learning hybrid representation by robust dictionary learning in factorized compressed space"):
  with members:
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "discriminative local sparse representation by robust adaptive dictionary pair learning"
    - "joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning"
    - "flexible auto weighted local coordinate concept factorization a robust framework for unsupervised clustering"
    - "fast low rank shared dictionary learning for image classification"

Connected through hyperedge HE-5 (induced by source paper "just go with the flow self supervised scene flow estimation"):
  with members:
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "learning correspondence from the cycle consistency of time"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "unsupervised multi task feature learning on point clouds"
    - "deep lidar cnn to understand the dynamics of moving vehicles"
    - "flownet3d learning scene flow in 3d point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "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"
    - "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"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

Connected through hyperedge HE-7 (induced by source paper "flownet3d learning scene flow in 3d point clouds"):
  with members:
    - "motion based object segmentation based on dense rgb d scene flow"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "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"
    - "tensorflow a system for large scale machine learning"
    - "shapenet an information rich 3d model repository"
    - "3d shapenets a deep representation for volumetric shapes"

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)