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: "a robust 3d 2d interactive tool for scene segmentation and annotation"
  Abstract: Recent advances of 3D acquisition devices have enabled large-scale acquisition of 3D scene data. Such data, if completely and well annotated, can serve as useful ingredients for a wide spectrum of computer vision and graphics works such as data-driven modeling and scene understanding, object detection and recognition. However, annotating a vast amount of 3D scene data remains challenging due to the lack of an effective tool and/or the complexity of 3D scenes (e.g. clutter, varying illumination conditions). This paper aims to build a robust annotation tool that effectively and conveniently enables the segmentation and annotation of massive 3D data. Our tool works by coupling 2D and 3D information via an interactive framework, through which users can provide high-level semantic annotation for objects. We have experimented our tool and found that a typical indoor scene could be well segmented and annotated in less than 30 minutes by using the tool, as opposed to a few hours if done manually. Along with the tool, we created a dataset of over a hundred 3D scenes associated with complete annotations using our tool. The tool and dataset are available at this http URL.

Connected through hyperedge HE-1 (induced by source paper "3d scene graph a structure for unified semantics 3d space and camera"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "gibson env real world perception for embodied agents"
    - "a deep learning based behavioral approach to indoor autonomous navigation"
    - "learning human object interactions by graph parsing neural networks"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "blitznet a real time deep network for scene understanding"

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 "semi automatic labeling for deep learning in robotics"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "focal loss for dense object detection"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "adversarial discriminative domain adaptation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"

Connected through hyperedge HE-4 (induced by source paper "annotation scaffolds for object modeling and manipulation"):
  with members:
    - "baxter s homunculus virtual reality spaces for teleoperation in manufacturing"
    - "shapenet an information rich 3d model repository"
    - "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds"
    - "data driven grasp synthesis a survey"
    - "visualizing and understanding convolutional networks"
    - "toric degenerations of bezier patches"

Connected through hyperedge HE-5 (induced by source paper "pointwise convolutional neural networks"):
  with members:
    - "pu net point cloud upsampling network"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "self normalizing neural networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning going beyond euclidean data"
    - "semantic scene completion from a single depth image"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "octnet learning deep 3d representations at high resolutions"

Connected through hyperedge HE-6 (induced by source paper "scannet richly annotated 3d reconstructions of indoor scenes"):
  with members:
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "semantic scene completion from a single depth image"
    - "learning to navigate the energy landscape"
    - "a large dataset of object scans"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "volumetric and multi view cnns for object classification on 3d data"
    - "shape completion using 3d encoder predictor cnns and shape synthesis"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "point wise convolutional neural network"):
  with members:
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "semantic scene completion from a single depth image"
    - "octnet learning deep 3d representations at high resolutions"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "3d shapenets a deep representation for volumetric shapes"

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)