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: "real time progressive 3d semantic segmentation for indoor scene"
  Abstract: The widespread adoption of autonomous systems such as drones and assistant robots has created a need for real-time high-quality semantic scene segmentation. In this paper, we propose an efficient yet robust technique for on-the-fly dense reconstruction and semantic segmentation of 3D indoor scenes. To guarantee real-time performance, our method is built atop small clusters of voxels and a conditional random field with higher-order constraints from structural and object cues, enabling progressive dense semantic segmentation without any precomputation. We extensively evaluate our method on different indoor scenes including kitchens, offices, and bedrooms in the SceneNN and ScanNet datasets and show that our technique consistently produces state-of-the-art segmentation results in both qualitative and quantitative experiments.

Connected through hyperedge HE-1 (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"
    - "pointconv deep convolutional networks on 3d point clouds"

Connected through hyperedge HE-2 (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"
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "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"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"

Connected through hyperedge HE-3 (induced by source paper "jsnet joint instance and semantic segmentation of 3d point clouds"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "attention guided unified network for panoptic segmentation"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep slam object level rgbd slam based on class specific deep shape priors"):
  with members:
    - "dense object reconstruction from rgbd images with embedded deep shape representations"
    - "fusion volumetric object level slam"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "incremental object database building 3d models from multiple partial observations"
    - "octnetfusion learning depth fusion from data"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "semantic photometric bundle adjustment on natural sequences"

Connected through hyperedge HE-5 (induced by source paper "volumetric instance aware semantic mapping and 3d object discovery"):
  with members:
    - "fast and accurate semantic mapping through geometric based incremental segmentation"
    - "fusion volumetric object level slam"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "incremental object database building 3d models from multiple partial observations"
    - "scenecut joint geometric and object segmentation for indoor scenes"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "learning to segment every thing"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-6 (induced by source paper "fusion volumetric object level slam"):
  with members:
    - "maskfusion real time recognition tracking and reconstruction of multiple moving objects"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "mask r cnn"
    - "meaningful maps with object oriented semantic mapping"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "monocular slam supported object recognition"

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)