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: "key ingredients of self driving cars"
  Abstract: Over the past decade, many research articles have been published in the area of autonomous driving. However, most of them focus only on a specific technological area, such as visual environment perception, vehicle control, etc. Furthermore, due to fast advances in the self-driving car technology, such articles become obsolete very fast. In this paper, we give a brief but comprehensive overview on key ingredients of autonomous cars (ACs), including driving automation levels, AC sensors, AC software, open source datasets, industry leaders, AC applications and existing challenges.

Connected through hyperedge HE-1 (induced by source paper "attacking vision based perception in end to end autonomous driving models"):
  with members:
    - "requirements driven test generation for autonomous vehicles with machine learning components"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "towards autonomous reinforcement learning automatic setting of hyper parameters using bayesian optimization"
    - "semantic adversarial deep learning"
    - "baidu apollo em motion planner"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-2 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation"):
  with members:
    - "pothole detection based on disparity transformation and road surface modeling"
    - "a robust pavement mapping system based on normal constrained stereo visual odometry"
    - "real time dense stereo embedded in a uav for road inspection"
    - "road damage detection based on unsupervised disparity map segmentation"
    - "scene understanding networks for autonomous driving based on around view monitoring system"
    - "a general pipeline for 3d detection of vehicles"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "real time stereo vision based lane detection system"

Connected through hyperedge HE-3 (induced by source paper "a robust pavement mapping system based on normal constrained stereo visual odometry"):
  with members:
    - "pothole detection based on disparity transformation and road surface modeling"
    - "a robust roll angle estimation algorithm based on gradient descent"
    - "real time dense stereo embedded in a uav for road inspection"
    - "road damage detection based on unsupervised disparity map segmentation"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "a novel disparity transformation algorithm for road segmentation"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-4 (induced by source paper "robust lane marking detection algorithm using drivable area segmentation and extended slt"):
  with members:
    - "pothole detection based on disparity transformation and road surface modeling"
    - "real time dense stereo embedded in a uav for road inspection"
    - "road damage detection based on unsupervised disparity map segmentation"
    - "real time subpixel fast bilateral stereo"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "real time stereo vision based lane detection system"
    - "a novel disparity transformation algorithm for road segmentation"
    - "multiple lane detection algorithm based on optimised dense disparity map estimation"

Connected through hyperedge HE-5 (induced by source paper "low cost gps aided lidar state estimation and map building"):
  with members:
    - "pothole detection based on disparity transformation and road surface modeling"
    - "road damage detection based on unsupervised disparity map segmentation"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "robust and precise vehicle localization based on multi sensor fusion in diverse city scenes"
    - "on manifold preintegration for real time visual inertial odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-6 (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"
    - "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"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-7 (induced by source paper "automatic calibration of dual lidars using two poles stickered with retro reflective tape"):
  with members:
    - "automatic calibration of multiple 3d lidars in urban environments"
    - "low cost gps aided lidar state estimation and map building"
    - "real time environmentally robust 3d lidar localization"
    - "a novel dual lidar calibration algorithm using planar surfaces"

Connected through hyperedge HE-8 (induced by source paper "autonomous uav landing system based on visual navigation"):
  with members:
    - "pothole detection based on disparity transformation and road surface modeling"
    - "real time dense stereo embedded in a uav for road inspection"
    - "road damage detection based on unsupervised disparity map segmentation"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"

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)