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: "an end to end system for crowdsourced 3d maps for autonomous vehicles the mapping component"
  Abstract: Autonomous vehicles rely on precise high definition (HD) 3d maps for navigation. This paper presents the mapping component of an end-to-end system for crowdsourcing precise 3d maps with semantically meaningful landmarks such as traffic signs (6 dof pose, shape and size) and traffic lanes (3d splines). The system uses consumer grade parts, and in particular, relies on a single front facing camera and a consumer grade GPS. Using real-time sign and lane triangulation on-device in the vehicle, with offline sign/lane clustering across multiple journeys and offline Bundle Adjustment across multiple journeys in the backend, we construct maps with mean absolute accuracy at sign corners of less than 20 cm from 25 journeys. To the best of our knowledge, this is the first end-to-end HD mapping pipeline in global coordinates in the automotive context using cost effective sensors.

Connected through hyperedge HE-1 (induced by source paper "simultaneous traffic sign detection and boundary estimation using convolutional neural network"):
  with members:
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "feature pyramid networks for object detection"
    - "exploring context with deep structured models for semantic segmentation"
    - "r fcn object detection via region based fully convolutional networks"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"

Connected through hyperedge HE-2 (induced by source paper "real time dynamic object detection for autonomous driving using prior 3d maps"):
  with members:
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "complex yolo real time 3d object detection on point clouds"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "carla an open urban driving simulator"
    - "cnn for very fast ground segmentation in velodyne lidar data"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "modnet moving object detection network with motion and appearance for autonomous driving"

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.RO(Robotics)