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: "gspn generative shape proposal network for 3d instance segmentation in point cloud"
  Abstract: We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data. Instead of treating object proposal as a direct bounding box regression problem, we take an analysis-by-synthesis strategy and generate proposals by reconstructing shapes from noisy observations in a scene. We incorporate GSPN into a novel 3D instance segmentation framework named Region-based PointNet (R-PointNet) which allows flexible proposal refinement and instance segmentation generation. We achieve state-of-the-art performance on several 3D instance segmentation tasks. The success of GSPN largely comes from its emphasis on geometric understandings during object proposal, which greatly reducing proposals with low objectness.

Connected through hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "shape2motion joint analysis of motion parts and attributes from 3d shapes"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "pixor real time 3d object detection from point clouds"
    - "densepoint learning densely contextual representation for efficient point cloud processing"

Connected through hyperedge HE-2 (induced by source paper "occuseg occupancy aware 3d instance segmentation"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "associatively segmenting instances and semantics in point clouds"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "3d instance segmentation via multi task metric learning"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"

Connected through hyperedge HE-3 (induced by source paper "bi directional attention for joint instance and semantic segmentation in point clouds"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "3d bird s eye view instance segmentation"
    - "jsnet joint instance and semantic segmentation of 3d point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-4 (induced by source paper "imvotenet boosting 3d object detection in point clouds with image votes"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "what makes training multi modal networks hard"
    - "deep hough voting for 3d object detection in point clouds"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "pointpillars fast encoders for object detection from point clouds"
    - "the sound of pixels"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "pointrcnn 3d object proposal generation and detection from point cloud"

Connected through hyperedge HE-5 (induced by source paper "learning and memorizing representative prototypes for 3d point cloud semantic and instance segmentation"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "3d bird s eye view instance segmentation"
    - "kpconv flexible and deformable convolution for point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "large scale long tailed recognition in an open world"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "3d instance segmentation via multi task metric learning"
    - "deep hough voting for 3d object detection in point clouds"

Connected through hyperedge HE-6 (induced by source paper "3dcfs fast and robust joint 3d semantic instance segmentation via coupled feature selection"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "3d instance segmentation via multi task metric learning"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"

Connected through hyperedge HE-7 (induced by source paper "fusion aware point convolution for online semantic 3d scene segmentation"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "active scene understanding via online semantic reconstruction"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "real time progressive 3d semantic segmentation for indoor scene"

Connected through hyperedge HE-8 (induced by source paper "revealnet seeing behind objects in rgb d scans"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "3d bird s eye view instance segmentation"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "3d instance segmentation via multi task metric learning"
    - "deep hough voting for 3d object detection in point clouds"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "sgpn similarity group proposal network for 3d point cloud instance 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)