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: "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

Connected through hyperedge HE-1 (induced by source paper "real time out of distribution detection in learning enabled cyber physical systems"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "deep anomaly detection with outlier exposure"
    - "robustness to out of distribution inputs via task aware generative uncertainty"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "carla an open urban driving simulator"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-2 (induced by source paper "dynamic weighted simplex strategy for learning enabled cyber physical systems"):
  with members:
    - "vision based control of a quadrotor in user proximity mediated vs end to end learning approaches"
    - "reachable set computation and safety verification for neural networks with relu activations"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deeppicar a low cost deep neural network based autonomous car"
    - "a component based simplex architecture for high assurance cyber physical systems"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "deepdriving learning affordance for direct perception in autonomous driving"

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.LG(Machine Learning)