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 "verifiably safe off model reinforcement learning"):
  Abstract: The desire to use reinforcement learning in safety-critical settings has inspired a recent interest in formal methods for learning algorithms. Existing formal methods for learning and optimization primarily consider the problem of constrained learning or constrained optimization. Given a single correct model and associated safety constraint, these approaches guarantee efficient learning while provably avoiding behaviors outside the safety constraint. Acting well given an accurate environmental model is an important pre-requisite for safe learning, but is ultimately insufficient for systems that operate in complex heterogeneous environments. This paper introduces verification-preserving model updates, the first approach toward obtaining formal safety guarantees for reinforcement learning in settings where multiple possible environmental models must be taken into account. Through a combination of inductive data and deductive proving with design-time model updates and runtime model falsification, we provide a first approach toward obtaining formal safety proofs for autonomous systems acting in heterogeneous environments.

Includes members:
  Member "logically correct reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safe reinforcement learning via probabilistic shields")
  Member "verifying controllers against adversarial examples with bayesian optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "composing diverse policies for temporally extended tasks")
  Member "shielded decision making in mdps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cautious reinforcement learning with logical constraints")
  Member "falsification of cyber physical systems using deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for cyber security")
  Member "a complete uniform substitution calculus for differential dynamic logic":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "toward structured proofs for dynamic logics")
  Member "planning fast and slow a framework for adaptive real time safe trajectory planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "real time funnel generation for restricted motion planning")
  Member "fastrack a modular framework for fast and guaranteed safe motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty")
  Member "constrained policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")

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.AI(Artificial Intelligence)