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 sis 3d semantic instance segmentation of rgb d scans"
  Abstract: We introduce 3D-SIS, a novel neural network architecture for 3D semantic instance segmentation in commodity RGB-D scans. The core idea of our method is to jointly learn from both geometric and color signal, thus enabling accurate instance predictions. Rather than operate solely on 2D frames, we observe that most computer vision applications have multi-view RGB-D input available, which we leverage to construct an approach for 3D instance segmentation that effectively fuses together these multi-modal inputs. Our network leverages high-resolution RGB input by associating 2D images with the volumetric grid based on the pose alignment of the 3D reconstruction. For each image, we first extract 2D features for each pixel with a series of 2D convolutions; we then backproject the resulting feature vector to the associated voxel in the 3D grid. This combination of 2D and 3D feature learning allows significantly higher accuracy object detection and instance segmentation than state-of-the-art alternatives. We show results on both synthetic and real-world public benchmarks, achieving an improvement in mAP of over 13 on real-world data.

Connected through hyperedge HE-1 (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"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "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-2 (induced by source paper "deep hough voting for 3d object detection in point clouds"):
  with members:
    - "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"
    - "dynamic graph cnn for learning on point clouds"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "semi convolutional operators for instance segmentation"
    - "tangent convolutions for dense prediction in 3d"
    - "pointrcnn 3d object proposal generation and detection from point cloud"

Connected through hyperedge HE-3 (induced by source paper "multi view pointnet for 3d scene understanding"):
  with members:
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "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-4 (induced by source paper "panopticfusion online volumetric semantic mapping at the level of stuff and things"):
  with members:
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "fusion volumetric object level slam"
    - "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-5 (induced by source paper "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"):
  with members:
    - "dynamic graph cnn for learning on point clouds"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "focal loss for dense object detection"
    - "recurrent pixel embedding for instance grouping"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "segcloud semantic segmentation of 3d point clouds"

Connected through hyperedge HE-6 (induced by source paper "masc multi scale affinity with sparse convolution for 3d instance segmentation"):
  with members:
    - "affinity derivation and graph merge for instance segmentation"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

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)