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: "semanticpaint interactive 3d labeling and learning at your fingertips"
  Abstract: We present a new interactive and online approach to 3D scene understanding. Our system, SemanticPaint, allows users to simultaneously scan their environment whilst interactively segmenting the scene simply by reaching out and touching any desired object or surface. Our system continuously learns from these segmentations, and labels new unseen parts of the environment. Unlike offline systems where capture, labeling, and batch learning often take hours or even days to perform, our approach is fully online. This provides users with continuous live feedback of the recognition during capture, allowing to immediately correct errors in the segmentation and/or learning—a feature that has so far been unavailable to batch and offline methods. This leads to models that are tailored or personalized specifically to the user's environments and object classes of interest, opening up the potential for new applications in augmented reality, interior design, and human/robot navigation. It also provides the ability to capture substantial labeled 3D datasets for training large-scale visual recognition systems.

Connected through hyperedge HE-1 (induced by source paper "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"
    - "relative geometry aware siamese neural network for 6dof camera relocalization"
    - "adversarial joint image and pose distribution learning for camera pose regression and refinement"
    - "deep auxiliary learning for visual localization and odometry"
    - "scene coordinate regression with angle based reprojection loss for camera relocalization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

Connected through hyperedge HE-2 (induced by source paper "revisiting point cloud classification a new benchmark dataset and classification model on real world data"):
  with members:
    - "flex convolution million scale point cloud learning beyond grid worlds"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "local spectral graph convolution for point set feature learning"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "deep sets"
    - "learning to drive from simulation without real world labels"

Connected through hyperedge HE-3 (induced by source paper "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "scene coordinate regression with angle based reprojection loss for camera relocalization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"
    - "full frame scene coordinate regression for image based localization"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-4 (induced by source paper "shooting labels 3d semantic labeling by virtual reality"):
  with members:
    - "open3d a modern library for 3d data processing"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "matterport3d learning from rgb d data in indoor environments"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "easylabel a semi automatic pixel wise object annotation tool for creating robotic rgb d datasets"):
  with members:
    - "scenecut joint geometric and object segmentation for indoor scenes"
    - "recognizing objects in the wild where do we stand"
    - "labelfusion a pipeline for generating ground truth labels for real rgbd data of cluttered scenes"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "rgbd datasets past present and future"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"

Connected through hyperedge HE-6 (induced by source paper "real time progressive 3d semantic segmentation for indoor scene"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "dynamic graph cnn for learning on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "deep projective 3d semantic segmentation"
    - "real time semantic image segmentation via spatial sparsity"
    - "semantic 3d occupancy mapping through efficient high order crfs"

Connected through hyperedge HE-7 (induced by source paper "tangent convolutions for dense prediction in 3d"):
  with members:
    - "open3d a modern library for 3d data processing"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "octnetfusion learning depth fusion from data"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "learning compact geometric features"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

Connected through hyperedge HE-8 (induced by source paper "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"):
  with members:
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"

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)