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: "scancomplete large scale scene completion and semantic segmentation for 3d scans"
  Abstract: We introduce ScanComplete, a novel data-driven approach for taking an incomplete 3D scan of a scene as input and predicting a complete 3D model along with per-voxel semantic labels. The key contribution of our method is its ability to handle large scenes with varying spatial extent, managing the cubic growth in data size as scene size increases. To this end, we devise a fully-convolutional generative 3D CNN model whose filter kernels are invariant to the overall scene size. The model can be trained on scene subvolumes but deployed on arbitrarily large scenes at test time. In addition, we propose a coarse-to-fine inference strategy in order to produce high-resolution output while also leveraging large input context sizes. In an extensive series of experiments, we carefully evaluate different model design choices, considering both deterministic and probabilistic models for completion and semantic inference. Our results show that we outperform other methods not only in the size of the environments handled and processing efficiency, but also with regard to completion quality and semantic segmentation performance by a significant margin.

Connected through hyperedge HE-1 (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-2 (induced by source paper "3d gated recurrent fusion for semantic scene completion"):
  with members:
    - "rgbd based dimensional decomposition residual network for 3d semantic scene completion"
    - "adaptive fusion for rgb d salient object detection"
    - "efficient semantic scene completion network with spatial group convolution"
    - "scalable place recognition under appearance change for autonomous driving"
    - "two stream 3d semantic scene completion"
    - "unsupervised rgbd video object segmentation using gans"
    - "rgb d based action recognition with light weight 3d convolutional networks"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"

Connected through hyperedge HE-3 (induced by source paper "routedfusion learning real time depth map fusion"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "surfelmeshing online surfel based mesh reconstruction"
    - "mesh r cnn"
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-4 (induced by source paper "comparing view based and map based semantic labelling in real time slam"):
  with members:
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "orthographic feature transform for monocular 3d object detection"
    - "octnetfusion learning depth fusion from data"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

Connected through hyperedge HE-5 (induced by source paper "efficient semantic scene completion network with spatial group convolution"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "inference learning and attention mechanisms that exploit and preserve sparsity in convolutional networks"
    - "3dcnn dqn rnn a deep reinforcement learning framework for semantic parsing of large scale 3d point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "ddsl deep differentiable simplex layer for learning geometric signals"):
  with members:
    - "spherical cnns on unstructured grids"
    - "convolutional neural networks on non uniform geometrical signals using euclidean spectral transformation"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "learning category specific mesh reconstruction from image collections"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "unsupervised training for 3d morphable model regression"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"

Connected through hyperedge HE-8 (induced by source paper "data efficient learning for sim to real robotic grasping using deep point cloud prediction networks"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "grasp2vec learning object representations from self supervised grasping"
    - "3d shape perception from monocular vision touch and shape priors"
    - "multiresolution tree networks for 3d point cloud processing"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"

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)