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: "simulation based adversarial test generation for autonomous vehicles with machine learning components"
  Abstract: Many organizations are developing autonomous driving systems, which are expected to be deployed at a large scale in the near future. Despite this, there is a lack of agreement on appropriate methods to test, debug, and certify the performance of these systems. One of the main challenges is that many autonomous driving systems have machine learning components, such as deep neural networks, for which formal properties are difficult to characterize. We present a testing framework that is compatible with test case generation and automatic falsification methods, which are used to evaluate cyber-physical systems. We demonstrate how the framework can be used to evaluate closed-loop properties of an autonomous driving system model that includes the ML components, all within a virtual environment. We demonstrate how to use test case generation methods, such as covering arrays, as well as requirement falsification methods to automatically identify problematic test scenarios. The resulting framework can be used to increase the reliability of autonomous driving systems.

Connected through hyperedge HE-1 (induced by source paper "reliability validation of learning enabled vehicle tracking"):
  with members:
    - "test metrics for recurrent neural networks"
    - "reasoning about cognitive trust in stochastic multiagent systems"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "detecting and tracking small moving objects in wide area motion imagery wami using convolutional neural networks cnns"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"

Connected through hyperedge HE-2 (induced by source paper "real time out of distribution detection in learning enabled cyber physical systems"):
  with members:
    - "attacking vision based perception in end to end autonomous driving models"
    - "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"
    - "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-3 (induced by source paper "attacking vision based perception in end to end autonomous driving models"):
  with members:
    - "key ingredients of self driving cars"
    - "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"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-4 (induced by source paper "comparing offline and online testing of deep neural networks an autonomous car case study"):
  with members:
    - "machine learning testing survey landscapes and horizons"
    - "paracosm a language and tool for testing autonomous driving systems"
    - "deepbillboard systematic physical world testing of autonomous driving systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "guiding deep learning system testing using surprise adequacy"
    - "carla an open urban driving simulator"
    - "systematic testing of convolutional neural networks for autonomous driving"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-5 (induced by source paper "analyzing cyber physical systems from the perspective of artificial intelligence"):
  with members:
    - "adversarial resilience learning towards systemic vulnerability analysis for large and complex systems"
    - "gray box adversarial testing for control systems with machine learning component"
    - "co simulation state of the art"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "practical black box attacks against machine learning"
    - "towards verified artificial intelligence"

Connected through hyperedge HE-6 (induced by source paper "simple physical adversarial examples against end to end autonomous driving models"):
  with members:
    - "semantic adversarial deep learning"
    - "baidu apollo em motion planner"
    - "robust physical world attacks on deep learning models"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "carla an open urban driving simulator"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-7 (induced by source paper "verifai a toolkit for the design and analysis of artificial intelligence based systems"):
  with members:
    - "scenic a language for scenario specification and scene generation"
    - "counterexample guided data augmentation"
    - "semantic adversarial deep learning"
    - "feature guided black box safety testing of deep neural networks"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "safe reinforcement learning via shielding"
    - "proximal policy optimization algorithms"
    - "towards verified artificial intelligence"

Connected through hyperedge HE-8 (induced by source paper "efficient autonomy validation in simulation with adaptive stress testing"):
  with members:
    - "adaptive stress testing for autonomous vehicles"
    - "scalable end to end autonomous vehicle testing via rare event simulation"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

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.SY(Systems and Control)