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 "meta learning within projective simulation"):
  Abstract: Learning models of artificial intelligence can nowadays perform very well on a large variety of tasks. However, in practice, different task environments are best handled by different learning models, rather than a single universal approach. Most non-trivial models thus require the adjustment of several to many learning parameters, which is often done on a case-by-case basis by an external party. Meta-learning refers to the ability of an agent to autonomously and dynamically adjust its own learning parameters or meta-parameters. In this paper, we show how projective simulation, a recently developed model of artificial intelligence, can naturally be extended to account for meta-learning in reinforcement learning settings. The projective simulation approach is based on a random walk process over a network of clips. The suggested meta-learning scheme builds upon the same design and employs clip networks to monitor the agent’s performance and to adjust its meta-parameters on the fly. We distinguish between reflex-type adaptation and adaptation through learning, and show the utility of both approaches. In addition, a trade-off between flexibility and learning-time is addressed. The extended model is examined on three different kinds of reinforcement learning tasks, in which the agent has different optimal values of the meta-parameters, and is shown to perform well, reaching near-optimal to optimal success rates in all of them, without ever needing to manually adjust any meta-parameter.

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 "hierarchical haptic manipulation for complex skill learning":
    also connected through other hyperedges:
      (none)
  Member "projective simulation applied to the grid world and the mountain car problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aerial animal biometrics individual friesian cattle recovery and visual identification via an autonomous uav with onboard deep inference")
  Member "recommending learning algorithms and their associated hyperparameters":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "task2vec task embedding for meta learning")
  Member "auto weka combined selection and hyperparameter optimization of classification algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats")
  Member "bandit problems with side observations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bounded regret for finitely parameterized multi armed bandits")
  Member "learning to learn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine 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.AI(Artificial Intelligence)