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 "attacking vision based perception in end to end autonomous driving models"):
  Abstract: Recent advances in machine learning, especially techniques such as deep neural networks, are enabling a range of emerging applications. One such example is autonomous driving, which often relies on deep learning for perception. However, deep learning-based perception has been shown to be vulnerable to a host of subtle adversarial manipulations of images. Nevertheless, the vast majority of such demonstrations focus on perception that is disembodied from end-to-end control. We present novel end-to-end attacks on autonomous driving in simulation, using simple physically realizable attacks: the painting of black lines on the road. These attacks target deep neural network models for end-to-end autonomous driving control. A systematic investigation shows that such attacks are easy to engineer, and we describe scenarios (e.g., right turns) in which they are highly effective. We define several objective functions that quantify the success of an attack and develop techniques based on Bayesian Optimization to efficiently traverse the search space of higher dimensional attacks. Additionally, we define a novel class of hijacking attacks, where painted lines on the road cause the driver-less car to follow a target path. Through the use of network deconvolution, we provide insights into the successful attacks, which appear to work by mimicking activations of entirely different scenarios. Our code is available at this https URL

Includes members:
  Member "key ingredients of self driving cars":
    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 "requirements driven test generation for autonomous vehicles with machine learning components":
    also connected through other hyperedges:
      (none)
  Member "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems")
  Member "simple physical adversarial examples against end to end autonomous driving models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "trust but verify an information theoretic explanation for the adversarial fragility of machine learning systems and a general defense against adversarial attacks")
  Member "towards autonomous reinforcement learning automatic setting of hyper parameters using bayesian optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic implicit natural gradient for black box optimization")
  Member "semantic adversarial deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "baidu apollo em motion planner":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "overview of tools supporting planning for automated driving")
  Member "simulation based adversarial test generation for autonomous vehicles with machine learning components":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reliability validation of learning enabled vehicle tracking")

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.LG(Machine Learning)