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 "neural simplex architecture"):
  Abstract: We present the Neural Simplex Architecture (NSA), a new approach to runtime assurance that provides safety guarantees for neural controllers (obtained e.g. using reinforcement learning) of complex autonomous and other cyber-physical systems without unduly sacrificing performance. NSA is inspired by the Simplex control architecture of Sha et al., but with some significant differences. In the traditional Simplex approach, the advanced controller (AC) is treated as a black box; there are no techniques for correcting the AC after it generates a potentially unsafe control input that causes a failover to the BC. Our NSA addresses this limitation. NSA not only provides safety assurances for CPSs in the presence of a possibly faulty neural controller, but can also improve the safety of such a controller in an online setting via retraining, without degrading its performance. NSA also offers reverse switching strategies, which allow the AC to resume control of the system under reasonable conditions, allowing the mission to continue unabated. Our experimental results on several significant case studies, including a target-seeking ground rover navigating an obstacle field and a neural controller for an artificial pancreas system, demonstrate NSA's benefits.

Includes members:
  Member "safety aware adaptive reinforcement learning with applications to brushbot navigation":
    also connected through other hyperedges:
      (none)
  Member "reward constrained policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "constrained deep reinforcement learning for energy sustainable multi uav based random access iot networks with noma")
  Member "a lyapunov based approach to safe reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constrained upper confidence reinforcement learning")
  Member "verification for machine learning autonomy and neural networks survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges")
  Member "scalable trust region method for deep reinforcement learning using kronecker factored approximation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "mastering chess and shogi by self play with a general reinforcement learning algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "constrained policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "safe reinforcement learning via shielding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to walk in the real world with minimal human effort")

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)