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 "gray box adversarial testing for control systems with machine learning component"):
  Abstract: Neural Networks (NN) have been proposed in the past as an effective means for both modeling and control of systems with very complex dynamics. However, despite the extensive research, NN-based controllers have not been adopted by the industry for safety critical systems. The primary reason is that systems with learning based controllers are notoriously hard to test and verify. Even harder is the analysis of such systems against system-level specifications. In this paper, we provide a gradient based method for searching the input space of a closed-loop control system in order to find adversarial samples against some system-level requirements. Our experimental results show that combined with randomized search, our method outperforms Simulated Annealing optimization.

Includes members:
  Member "counterexample guided data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges")
  Member "reachability analysis and safety verification for neural network control systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "validation of image based neural network controllers through adaptive stress testing")
  Member "simulation based adversarial test generation for autonomous vehicles with machine learning components":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reliability validation of learning enabled vehicle tracking")
  Member "output range analysis for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "systematic testing of convolutional neural networks for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a formalization of robustness for deep neural networks")
  Member "compositional falsification of cyber physical systems with machine learning components":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "teaching temporal logics to neural networks")
  Member "towards verified artificial intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "safety verification of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "overfitting in adversarially robust deep learning")

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)