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: "floornet a unified framework for floorplan reconstruction from 3d scans"
  Abstract: The ultimate goal of this indoor mapping research is to automatically reconstruct a floorplan simply by walking through a house with a smartphone in a pocket. This paper tackles this problem by proposing FloorNet, a novel deep neural architecture. The challenge lies in the processing of RGBD streams spanning a large 3D space. FloorNet effectively processes the data through three neural network branches: 1) PointNet with 3D points, exploiting the 3D information; 2) CNN with a 2D point density image in a top-down view, enhancing the local spatial reasoning; and 3) CNN with RGB images, utilizing the full image information. FloorNet exchanges intermediate features across the branches to exploit the best of all the architectures. We have created a benchmark for floorplan reconstruction by acquiring RGBD video streams for 155 residential houses or apartments with Google Tango phones and annotating complete floorplan information. Our qualitative and quantitative evaluations demonstrate that the fusion of three branches effectively improves the reconstruction quality. We hope that the paper together with the benchmark will be an important step towards solving a challenging vector-graphics reconstruction problem. Code and data are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans"):
  with members:
    - "teaser fast and certifiable point cloud registration"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "semi supervised semantic mapping through label propagation with semantic texture meshes"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "value iteration networks on multiple levels of abstraction"

Connected through hyperedge HE-2 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "edge semantic learning strategy for layout estimation in indoor environment"
    - "flat2layout flat representation for estimating layout of general room types"
    - "structured3d a large photo realistic dataset for structured 3d modeling"
    - "deep hough voting for 3d object detection in point clouds"
    - "deepperimeter indoor boundary estimation from posed monocular sequences"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"

Connected through hyperedge HE-3 (induced by source paper "indoor layout estimation by 2d lidar and camera fusion"):
  with members:
    - "edge semantic learning strategy for layout estimation in indoor environment"
    - "deepperimeter indoor boundary estimation from posed monocular sequences"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"

Connected through hyperedge HE-4 (induced by source paper "conv mpn convolutional message passing neural network for structured outdoor architecture reconstruction"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "ppgnet learning point pair graph for line segment detection"
    - "deep high resolution representation learning for human pose estimation"
    - "end to end wireframe parsing"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "spacenet a remote sensing dataset and challenge series"
    - "simple baselines for human pose estimation and tracking"
    - "monocular total capture posing face body and hands in the wild"

Connected through hyperedge HE-5 (induced by source paper "structured3d a large photo realistic dataset for structured 3d modeling"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "3d manhattan room layout reconstruction from a single 360 image"
    - "single image piece wise planar 3d reconstruction via associative embedding"
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "learning to reconstruct 3d manhattan wireframes from a single image"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "learning to adapt structured output space for semantic segmentation"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"

Connected through hyperedge HE-6 (induced by source paper "deepperimeter indoor boundary estimation from posed monocular sequences"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "estimating depth from rgb and sparse sensing"
    - "mvdepthnet real time multiview depth estimation neural network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "roomnet end to end room layout estimation"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-7 (induced by source paper "a unified point based framework for 3d segmentation"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-8 (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"
    - "3d sis 3d semantic instance segmentation of rgb d scans"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "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)