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 "low cost gps aided lidar state estimation and map building"):
  Abstract: Using different sensors in an autonomous vehicle (AV) can provide multiple constraints to optimize AV location estimation. In this paper, we present a low-cost GPS-assisted LiDAR state estimation system for AVs. Firstly, we utilize LiDAR to obtain highly precise 3D geometry data. Next, we use an inertial measurement unit (IMU) to correct point cloud misalignment caused by incorrect place recognition. The estimated LiDAR odometry and IMU measurement are then jointly optimized. We use a lost-cost GPS instead of a real-time kinematic (RTK) module to refine the estimated LiDAR-inertial odometry. Our low-cost GPS and LiDAR complement each other, and can provide highly accurate vehicle location information. Moreover, a low-cost GPS is much cheaper than an RTK module, which reduces the overall AV sensor cost. Our experimental results demonstrate that our proposed GPS-aided LiDAR-inertial odometry system performs very accurately. The accuracy achieved when processing a dataset collected in an industrial zone is approximately 0.14 m.

Includes members:
  Member "pothole detection based on disparity transformation and road surface modeling":
    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 "road damage detection based on unsupervised disparity map segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation")
  Member "road surface 3d reconstruction based on dense subpixel disparity map estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast depth estimation for view synthesis")
  Member "vins mono a robust and versatile monocular visual inertial state estimator":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep drone racing from simulation to reality with domain randomization")
  Member "robust and precise vehicle localization based on multi sensor fusion in diverse city scenes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "da4ad end to end deep attention aware features aided visual localization for autonomous driving")
  Member "on manifold preintegration for real time visual inertial odometry":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "orb slam a versatile and accurate monocular slam system":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)