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: "design of an autonomous racecar perception state estimation and system integration"
  Abstract: This paper introduces fl\"uela driverless: the first autonomous racecar to win a Formula Student Driverless competition. In this competition, among other challenges, an autonomous racecar is tasked to complete 10 laps of a previously unknown racetrack as fast as possible and using only onboard sensing and computing. The key components of fl\"uela's design are its modular redundant sub-systems that allow robust performance despite challenging perceptual conditions or partial system failures. The paper presents the integration of key components of our autonomous racecar, i.e., system design, EKF-based state estimation, LiDAR-based perception, and particle filter-based SLAM. We perform an extensive experimental evaluation on real-world data, demonstrating the system's effectiveness by outperforming the next-best ranking team by almost half the time required to finish a lap. The autonomous racecar reaches lateral and longitudinal accelerations comparable to those achieved by experienced human drivers.

Connected through hyperedge HE-1 (induced by source paper "fast and accurate mapping for autonomous racing"):
  with members:
    - "amz driverless the full autonomous racing system"
    - "tracking multiple moving objects using unscented kalman filtering techniques"
    - "yolov3 an incremental improvement"
    - "redundant perception and state estimation for reliable autonomous racing"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "sampling based algorithms for optimal motion planning using closed loop prediction"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-2 (induced by source paper "end to end velocity estimation for autonomous racing"):
  with members:
    - "amz driverless the full autonomous racing system"
    - "vision based high speed driving with a deep dynamic observer"
    - "redundant perception and state estimation for reliable autonomous racing"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "adadelta an adaptive learning rate method"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "explorations and lessons learned in building an autonomous formula sae car from simulations"):
  with members:
    - "closing the gap towards end to end autonomous vehicle system"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "real time dynamic object detection for autonomous driving using prior 3d maps"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "learning end to end autonomous driving using guided auxiliary supervision"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "amz driverless the full autonomous racing system"):
  with members:
    - "real time 3d traffic cone detection for autonomous driving"
    - "autorally an open platform for aggressive autonomous driving"
    - "redundant perception and state estimation for reliable autonomous racing"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "real time control for autonomous racing based on viability theory"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "a survey of motion planning and control techniques for self driving urban vehicles"

Connected through hyperedge HE-5 (induced by source paper "real time environmentally robust 3d lidar localization"):
  with members:
    - "a robust pavement mapping system based on normal constrained stereo visual odometry"
    - "key ingredients of self driving cars"
    - "appearance based landmark selection for efficient long term visual localization"
    - "map management for efficient long term visual localization in outdoor environments"
    - "laser map aided visual inertial localization in changing environment"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-6 (induced by source paper "from perception to control an autonomous driving system for a formula student driverless car"):
  with members:
    - "self driving cars a survey"
    - "yolov3 an incremental improvement"
    - "autonomous driving system design for formula student driverless racecar"

Connected through hyperedge HE-7 (induced by source paper "building a winning self driving car in six months"):
  with members:
    - "yolo9000 better faster stronger"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "redundant perception and state estimation for reliable autonomous racing"):
  with members:
    - "segmatch segment based place recognition in 3d point clouds"
    - "yolo9000 better faster stronger"

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)