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 "parot a practical framework for robust deep neural network training"):
  Abstract: Deep Neural Networks (DNNs) are finding important applications in safety-critical systems such as Autonomous Vehicles (AVs), where perceiving the environment correctly and robustly is necessary for safe operation. Raising unique challenges for assurance due to their black-box nature, DNNs pose a fundamental problem for regulatory acceptance of these types of systems. Robust training --- training to minimize excessive sensitivity to small changes in input --- has emerged as one promising technique to address this challenge. However, existing robust training tools are inconvenient to use or apply to existing codebases and models: they typically only support a small subset of model elements and require users to extensively rewrite the training code. In this paper we introduce a novel framework, PaRoT, developed on the popular TensorFlow platform, that greatly reduces the barrier to entry. Our framework enables robust training to be performed on arbitrary DNNs without any rewrites to the model. We demonstrate that our framework's performance is comparable to prior art, and exemplify its ease of use on off-the-shelf, trained models and on a real-world industrial application: training a robust traffic light detection network.

Includes members:
  Member "algorithms for verifying deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic adversarial perturbations using learnt representations")
  Member "a provable defense for deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards stable and efficient training of verifiably robust neural networks")
  Member "a convex relaxation barrier to tight robustness verification of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "scaling provable adversarial defenses":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "symbolic execution for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "importance driven deep learning system testing")
  Member "mixtrain scalable training of formally robust neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "using machine learning safely in automotive software an assessment and adaption of software process requirements in iso 26262":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a safety framework for critical systems utilising deep neural networks")
  Member "with friends like these who needs adversaries":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "auditing and debugging deep learning models via decision boundaries individual level and group level analysis")

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)