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: "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
  Abstract: We present a novel deep neural network architecture for end-to-end scene flow estimation that directly operates on large-scale 3D point clouds. Inspired by Bilateral Convolutional Layers (BCL), we propose novel DownBCL, UpBCL, and CorrBCL operations that restore structural information from unstructured point clouds, and fuse information from two consecutive point clouds. Operating on discrete and sparse permutohedral lattice points, our architectural design is parsimonious in computational cost. Our model can efficiently process a pair of point cloud frames at once with a maximum of 86K points per frame. Our approach achieves state-of-the-art performance on the FlyingThings3D and KITTI Scene Flow 2015 datasets. Moreover, trained on synthetic data, our approach shows great generalization ability on real-world data and on different point densities without fine-tuning.

Connected through hyperedge HE-1 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps"):
  with members:
    - "3d point cloud processing and learning for autonomous driving"
    - "point cloud processing via recurrent set encoding"
    - "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "pixor real time 3d object detection from point clouds"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "meteornet deep learning on dynamic 3d point cloud sequences"

Connected through hyperedge HE-2 (induced by source paper "grid gcn for fast and scalable point cloud learning"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "point cloud oversegmentation with graph structured deep metric learning"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "kpconv flexible and deformable convolution for point clouds"
    - "modeling point clouds with self attention and gumbel subset sampling"
    - "point voxel cnn for efficient 3d deep learning"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "relation shape convolutional neural network for point cloud analysis"

Connected through hyperedge HE-3 (induced by source paper "pointrnn point recurrent neural network for moving point cloud processing"):
  with members:
    - "kpconv flexible and deformable convolution for point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "cubic lstms for video prediction"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "relation shape convolutional neural network for point cloud analysis"
    - "deep hough voting for 3d object detection in point clouds"
    - "flownet3d learning scene flow in 3d point clouds"
    - "pointpillars fast encoders for object detection from point clouds"

Connected through hyperedge HE-4 (induced by source paper "latticenet fast point cloud segmentation using permutohedral lattices"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "group normalization"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "splatnet sparse lattice networks for point cloud processing"
    - "real time progressive 3d semantic segmentation for indoor scene"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"

Connected through hyperedge HE-5 (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:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "pointflownet learning representations for rigid motion estimation from 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-6 (induced by source paper "flownet3d geometric losses for deep scene flow estimation"):
  with members:
    - "deep rigid instance scene flow"
    - "pointflownet learning representations for rigid motion estimation from point clouds"
    - "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-7 (induced by source paper "just go with the flow self supervised scene flow estimation"):
  with members:
    - "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"
    - "pointflownet learning representations for rigid motion estimation from point clouds"
    - "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"

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)