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: "masc multi scale affinity with sparse convolution for 3d instance segmentation"
  Abstract: We propose a new approach for 3D instance segmentation based on sparse convolution and point affinity prediction, which indicates the likelihood of two points belonging to the same instance. The proposed network, built upon submanifold sparse convolution [3], processes a voxelized point cloud and predicts semantic scores for each occupied voxel as well as the affinity between neighboring voxels at different scales. A simple yet effective clustering algorithm segments points into instances based on the predicted affinity and the mesh topology. The semantic for each instance is determined by the semantic prediction. Experiments show that our method outperforms the state-of-the-art instance segmentation methods by a large margin on the widely used ScanNet benchmark [2]. We share our code publicly at this https URL.

Connected through hyperedge HE-1 (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"
    - "3d instance segmentation via multi task metric learning"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "deep hough voting for 3d object detection in point clouds"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "learning object bounding boxes for 3d instance segmentation on point clouds"):
  with members:
    - "3d bird s eye view instance segmentation"
    - "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"
    - "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-4 (induced by source paper "3d instance segmentation via multi task metric learning"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "pixor real time 3d object detection from point clouds"
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "3d sis 3d semantic instance segmentation of rgb d scans"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "semi convolutional operators for instance segmentation"

Connected through hyperedge HE-5 (induced by source paper "identifying unknown instances for autonomous driving"):
  with members:
    - "3d bird s eye view instance segmentation"
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "large scale long tailed recognition in an open world"
    - "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-6 (induced by source paper "multi view pointnet for 3d scene understanding"):
  with members:
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "3d sis 3d semantic instance segmentation of rgb d scans"
    - "splatnet sparse lattice networks for point cloud processing"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "pointconv deep convolutional networks on 3d 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-7 (induced by source paper "panopticfusion online volumetric semantic mapping at the level of stuff and things"):
  with members:
    - "fusion volumetric object level slam"
    - "3d sis 3d semantic instance segmentation of rgb d scans"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "tangent convolutions for dense prediction in 3d"
    - "mid fusion octree based object level multi instance dynamic slam"

Connected through hyperedge HE-8 (induced by source paper "rescan inductive instance segmentation for indoor rgbd scans"):
  with members:
    - "constructaide analyzing and visualizing construction sites through photographs and building models"
    - "detection and tracking of general movable objects in large 3d maps"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "matterport3d learning from rgb d data in indoor environments"
    - "the strands project long term autonomy in everyday environments"
    - "learning hierarchical object maps of non stationary environments with mobile robots"

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)