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: "dels 3d deep localization and segmentation with a 3d semantic map"
  Abstract: For applications such as autonomous driving, self-localization/camera pose estimation and scene parsing are crucial technologies. In this paper, we propose a unified framework to tackle these two problems simultaneously. The uniqueness of our design is a sensor fusion scheme which integrates camera videos, motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robustness and efficiency of the system. Specifically, we first have an initial coarse camera pose obtained from consumer-grade GPS/IMU, based on which a label map can be rendered from the 3D semantic map. Then, the rendered label map and the RGB image are jointly fed into a pose CNN, yielding a corrected camera pose. In addition, to incorporate temporal information, a multi-layer recurrent neural network (RNN) is further deployed improve the pose accuracy. Finally, based on the pose from RNN, we render a new label map, which is fed together with the RGB image into a segment CNN which produces per-pixel semantic label. In order to validate our approach, we build a dataset with registered 3D point clouds and video camera images. Both the point clouds and the images are semantically-labeled. Each video frame has ground truth pose from highly accurate motion sensors. We show that practically, pose estimation solely relying on images like PoseNet may fail due to street view confusion, and it is important to fuse multiple sensors. Finally, various ablation studies are performed, which demonstrate the effectiveness of the proposed system. In particular, we show that scene parsing and pose estimation are mutually beneficial to achieve a more robust and accurate system.

Connected through hyperedge HE-1 (induced by source paper "da4ad end to end deep attention aware features aided visual localization for autonomous driving"):
  with members:
    - "enforcenet monocular camera localization in large scale indoor sparse lidar point cloud"
    - "sparse to dense hypercolumn matching for long term visual localization"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "vizard reliable visual localization for autonomous vehicles in urban outdoor environments"
    - "deep auxiliary learning for visual localization and odometry"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "local descriptors optimized for average precision"

Connected through hyperedge HE-2 (induced by source paper "toronto 3d a large scale mobile lidar dataset for semantic segmentation of urban roadways"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "kpconv flexible and deformable convolution for point clouds"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "paris lille 3d a large and high quality ground truth urban point cloud dataset for automatic segmentation and classification"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

Connected through hyperedge HE-3 (induced by source paper "the apolloscape open dataset for autonomous driving and its application"):
  with members:
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deepmvs learning multi view stereopsis"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "path aggregation network for instance segmentation"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"

Connected through hyperedge HE-4 (induced by source paper "selectfusion a generic framework to selectively learn multisensory fusion"):
  with members:
    - "beyond tracking selecting memory and refining poses for deep visual odometry"
    - "selective sensor fusion for neural visual inertial odometry"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "lo net deep real time lidar odometry"
    - "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "the limits and potentials of deep learning for robotics"

Connected through hyperedge HE-5 (induced by source paper "selective sensor fusion for neural visual inertial odometry"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "the limits and potentials of deep learning for robotics"
    - "global pose estimation with an attention based recurrent network"
    - "modeling varying camera imu time offset in optimization based visual inertial odometry"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "the tum vi benchmark for evaluating visual inertial odometry"

Connected through hyperedge HE-6 (induced by source paper "pareto multi task learning"):
  with members:
    - "which tasks should be learned together in multi task learning"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "taskonomy disentangling task transfer learning"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "multi task learning as multi objective optimization"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "learning to multitask"

Connected through hyperedge HE-7 (induced by source paper "scalable place recognition under appearance change for autonomous driving"):
  with members:
    - "semi parametric topological memory for navigation"
    - "polysemous codes"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "geometry aware learning of maps for camera localization"

Connected through hyperedge HE-8 (induced by source paper "enforcenet monocular camera localization in large scale indoor sparse lidar point cloud"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "open3d a modern library for 3d data processing"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "learning to navigate in cities without a map"
    - "efficient neural architecture search via parameter sharing"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"

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)