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: "augmented reality meets computer vision efficient data generation for urban driving scenes"
  Abstract: The success of deep learning in computer vision is based on availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment real images with virtual objects. This allows us to create realistic composite images which exhibit both realistic background appearance and a large number of complex object arrangements. In contrast to modeling complete 3D environments, our augmentation approach requires only a few user interactions in combination with 3D shapes of the target object. Through extensive experimentation, we conclude the right set of parameters to produce augmented data which can maximally enhance the performance of instance segmentation models. Further, we demonstrate the utility of our approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenes. We test the models trained on our augmented data on the KITTI 2015 dataset, which we have annotated with pixel-accurate ground truth, and on Cityscapes dataset. Our experiments demonstrate that models trained on augmented imagery generalize better than those trained on synthetic data or models trained on limited amount of annotated real data.

Connected through hyperedge HE-1 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks"):
  with members:
    - "acfnet attentional class feature network for semantic segmentation"
    - "asymmetric non local neural networks for semantic segmentation"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "efficient segmentation learning downsampling near semantic boundaries"
    - "boundary aware feature propagation for scene segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a discriminative feature network for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-2 (induced by source paper "neural mesh refiner for 6 dof pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "albumentations fast and flexible image augmentations"
    - "how much position information do convolutional neural networks encode"
    - "disentangling monocular 3d object detection"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "6d object pose estimation based on 2d bounding box"

Connected through hyperedge HE-3 (induced by source paper "consac robust multi model fitting by conditional sample consensus"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "progressive x efficient anytime multi model fitting algorithm"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "multi class model fitting by energy minimization and mode seeking"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "learning to find good correspondences"

Connected through hyperedge HE-4 (induced by source paper "facelift a transparent deep learning framework to beautify urban scenes"):
  with members:
    - "disentangled person image generation"
    - "unsupervised cross domain image generation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "are safer looking neighborhoods more lively a multimodal investigation into urban life"
    - "deep learning the city quantifying urban perception at a global scale"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-5 (induced by source paper "soic semantic online initialization and calibration for lidar and camera"):
  with members:
    - "spatiotemporal camera lidar calibration a targetless and structureless approach"
    - "2d3d matchnet learning to match keypoints across 2d image and 3d point cloud"
    - "calibnet geometrically supervised extrinsic calibration using 3d spatial transformer networks"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "automatic extrinsic calibration for lidar stereo vehicle sensor setups"
    - "reflectance intensity assisted automatic and accurate extrinsic calibration of 3d lidar and panoramic camera using a printed chessboard"

Connected through hyperedge HE-6 (induced by source paper "a markerless deep learning based 6 degrees of freedom poseestimation for with mobile robots using rgb data"):
  with members:
    - "a 3d deep learning based augmented reality calibration method for robotic environments using depth sensor data"
    - "augmented reality based visualization of navigation data of mobile robots on the microsoft hololens possibilities and limitations"
    - "deep hough voting for 3d object detection in point clouds"
    - "estimating 6d pose from localizing designated surface keypoints"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "deep 6 dof tracking"
    - "real time seamless single shot 6d object pose prediction"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-7 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges"):
  with members:
    - "example and feature importance based explanations for black box machine learning models"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "visual interpretability for deep learning a survey"
    - "controlling over generalization and its effect on adversarial examples detection and generation"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "counterexample guided data augmentation"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "learning a discriminative feature network for semantic segmentation"
    - "deep depth completion of a single rgb d image"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "generative image inpainting with contextual attention"
    - "skip connections eliminate singularities"
    - "cycada cycle consistent adversarial domain adaptation"

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)