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: "scannet richly annotated 3d reconstructions of indoor scenes"
  Abstract: A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very little data is available -- current datasets cover a small range of scene views and have limited semantic annotations. To address this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations. To collect this data, we designed an easy-to-use and scalable RGB-D capture system that includes automated surface reconstruction and crowdsourced semantic annotation. We show that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks, including 3D object classification, semantic voxel labeling, and CAD model retrieval. The dataset is freely available at this http URL.

Connected through hyperedge HE-1 (induced by source paper "a review on deep learning techniques for 3d sensed data classification"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "context prediction for unsupervised deep learning on point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-2 (induced by source paper "3d object recognition with ensemble learning a study of point cloud based deep learning models"):
  with members:
    - "a deeper look at 3d shape classifiers"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-3 (induced by source paper "learning single image depth from videos using quality assessment networks"):
  with members:
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "soccer on your tabletop"
    - "megadepth learning single view depth prediction from internet photos"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-4 (induced by source paper "extreme relative pose estimation for rgb d scans via scene completion"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "open3d a modern library for 3d data processing"
    - "recurrent transformer networks for semantic correspondence"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "unsupervised learning of depth and ego motion from video"
    - "relative camera pose estimation using convolutional neural networks"
    - "matterport3d learning from rgb d data in indoor environments"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-5 (induced by source paper "planenet piece wise planar reconstruction from a single rgb image"):
  with members:
    - "dilated residual networks"
    - "roomnet end to end room layout estimation"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "learning shape abstractions by assembling volumetric primitives"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "depth from a single image by harmonizing overcomplete local network predictions"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-6 (induced by source paper "path invariant map networks"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "robust rotation synchronization via low rank and sparse matrix decomposition"
    - "data distillation towards omni supervised learning"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "dualgan unsupervised dual learning for image to image translation"
    - "semantic scene completion from a single depth image"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-7 (induced by source paper "object aware guidance for autonomous scene reconstruction"):
  with members:
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "semantic scene completion from a single depth image"
    - "motion planning strategies for autonomously mapping 3d structures"
    - "3dmatch learning local geometric descriptors from rgb d reconstructions"
    - "3dmatch learning the matching of local 3d geometry in range scans"
    - "semanticpaint interactive 3d labeling and learning at your fingertips"

Connected through hyperedge HE-8 (induced by source paper "recurrent slice networks for 3d segmentation on point clouds"):
  with members:
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"
    - "sparse 3d convolutional neural networks"
    - "spatially sparse convolutional neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

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)