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: "3d bird s eye view instance segmentation"
  Abstract: Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of object classification and semantic segmentation. However, the task of instance segmentation is currently less explored. In this work, we present 3D-BEVIS (3D bird’s-eye-view instance segmentation), a deep learning framework for joint semantic- and instance-segmentation on 3D point clouds. Following the idea of previous proposal-free instance segmentation approaches, our model learns a feature embedding and groups the obtained feature space into semantic instances. Current point-based methods process local sub-parts of a full scene independently, followed by a heuristic merging step. However, to perform instance segmentation by clustering on a full scene, globally consistent features are required. Therefore, we propose to combine local point geometry with global context information using an intermediate bird’s-eye view representation.

Connected through hyperedge HE-1 (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"
    - "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"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"

Connected through hyperedge HE-2 (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"
    - "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"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-3 (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"
    - "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"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "scanrefer 3d object localization in rgb d scans using natural language"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "dynamic graph attention for referring expression comprehension"
    - "embodied language grounding with implicit 3d visual feature representations"
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "rerere remote embodied referring expressions in real indoor environments"
    - "zero shot grounding of objects from natural language queries"
    - "cross modal self attention network for referring image segmentation"
    - "neural sequential phrase grounding seqground"

Connected through hyperedge HE-5 (induced by source paper "learning object bounding boxes for 3d instance segmentation on point clouds"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "kpconv flexible and deformable convolution for point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "deep hough voting for 3d object detection in point clouds"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"

Connected through hyperedge HE-6 (induced by source paper "identifying unknown instances for autonomous driving"):
  with members:
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "large scale long tailed recognition in an open world"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "4d generic video object proposals"
    - "tensormask a foundation for dense object segmentation"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "bayesian semantic instance segmentation in open set world"

Connected through hyperedge HE-7 (induced by source paper "dilated point convolutions on the receptive field size of point convolutions on 3d point clouds"):
  with members:
    - "point cloud oversegmentation with graph structured deep metric learning"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "so net self organizing network for point cloud analysis"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "know what your neighbors do 3d semantic segmentation of point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-8 (induced by source paper "dilated point convolutions on the receptive field of point convolutions"):
  with members:
    - "point cloud oversegmentation with graph structured deep metric learning"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "know what your neighbors do 3d semantic segmentation of point clouds"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "exploring spatial context for 3d semantic segmentation of point clouds"

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)