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 "benchmarking projective simulation in navigation problems"):
  Abstract: Projective simulation is a model for intelligent agents with a deliberation capacity that is based on episodic memory. The model has been shown to provide a flexible framework for constructing reinforcement-learning agents, and it allows for quantum mechanical generalization, which leads to a speedup in deliberation time. Projective simulation agents have been applied successfully in the context of complex skill learning in robotics and the design of the state-of-the-art quantum experiments. In this paper, we study the performance of projective simulation in two benchmarking problems in navigation, namely, the grid world and the mountain car problem. The performance of projective simulation is compared to standard tabular reinforcement learning approaches, Q-learning, and SARSA. Our comparison demonstrates that the performance of projective simulation and standard learning approaches are qualitatively and quantitatively similar, while it is much easier to choose optimal model parameters in the case of projective simulation with reduced computational effort of one to two orders of magnitude. Our results show that the projective simulation model stands out for its simplicity in terms of the number of model parameters, which makes it simple to set up the learning agent in unknown task environments.

Includes members:
  Member "projective simulation with generalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how a minimal learning agent can infer the existence of unobserved variables in a complex environment")
  Member "skill learning by autonomous robotic playing using active learning and creativity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the convergence of projective simulation based reinforcement learning in markov decision processes")
  Member "meta learning within projective simulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the convergence of projective simulation based reinforcement learning in markov decision processes")
  Member "an alternative softmax operator for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep rbf value functions for continuous control")
  Member "robotic playing for hierarchical complex skill learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self supervised damage avoiding manipulation strategy optimization via mental simulation")
  Member "learning to navigate in complex environments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "projective simulation applied to the grid world and the mountain car problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "aerial animal biometrics individual friesian cattle recovery and visual identification via an autonomous uav with onboard deep inference")

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)