human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "real time environmentally robust 3d lidar localization"):
  Abstract: Localization, or position fixing, is an important problem in robotics research. In this paper, we propose a novel approach for long-term localization in a changing environment using 3D LiDAR. We first create the map of a real environment using GPS and LiDAR. Then, we divide the map into several small parts as the targets for cloud registration, which can not only improve the robustness but also reduce the registration time. PointLocalization allows us to fuse different kinds of odometers, which can optimize the accuracy and frequency of localization results. We evaluate our algorithm on an unmanned ground vehicle (UGV) using LiDAR and a wheel encoder, and obtain the localization results at more than 20 Hz after fusion. The algorithm can also localize the UGV in a 180-degree field of view (FOV). Using an outdated map captured six months ago, this algorithm shows great robustness, and the test results show that it can achieve an accuracy of 10 cm. PointLocalization has been tested for a period of more than six months in a crowded factory and has operated successfully over a distance of more than 2000 km.

Includes members:
  Member "a robust pavement mapping system based on normal constrained stereo visual odometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation")
  Member "key ingredients of self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attacking vision based perception in end to end autonomous driving models")
  Member "appearance based landmark selection for efficient long term visual localization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "experimental comparison of visual aided odometry methods for rail vehicles")
  Member "map management for efficient long term visual localization in outdoor environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "don t worry about the weather unsupervised condition dependent domain adaptation")
  Member "laser map aided visual inertial localization in changing environment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "globally optimal consensus maximization for robust visual inertial localization in point and line map")
  Member "design of an autonomous racecar perception state estimation and system integration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast and accurate mapping for autonomous racing")
  Member "orb slam a versatile and accurate monocular slam system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")

Question: Please tell me which class the source paper 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)