human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "projective simulation with generalization"
  Abstract: The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and present a dynamic and autonomous machinery that enables projective simulation agents to meaningfully generalize. Projective simulation, a novel, physical approach to artificial intelligence, was recently shown to perform well in standard reinforcement learning problems, with applications in advanced robotics as well as quantum experiments. Both the basic projective simulation model and the presented generalization machinery are based on very simple principles. This allows us to provide a full analytical analysis of the agent’s performance and to illustrate the benefit the agent gains by generalizing. Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.

Connected through hyperedge HE-1 (induced by source paper "how a minimal learning agent can infer the existence of unobserved variables in a complex environment"):
  with members:
    - "benchmarking projective simulation in navigation problems"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "robotic playing for hierarchical complex skill learning"

Connected through hyperedge HE-2 (induced by source paper "on the convergence of projective simulation based reinforcement learning in markov decision processes"):
  with members:
    - "benchmarking projective simulation in navigation problems"
    - "skill learning by autonomous robotic playing using active learning and creativity"
    - "meta learning within projective simulation"

Connected through hyperedge HE-3 (induced by source paper "benchmarking projective simulation in navigation problems"):
  with members:
    - "skill learning by autonomous robotic playing using active learning and creativity"
    - "meta learning within projective simulation"
    - "an alternative softmax operator for reinforcement learning"
    - "robotic playing for hierarchical complex skill learning"
    - "learning to navigate in complex environments"
    - "projective simulation applied to the grid world and the mountain car problem"

Connected through hyperedge HE-4 (induced by source paper "skill learning by autonomous robotic playing using active learning and creativity"):
  with members:
    - "robotic playing for hierarchical complex skill learning"
    - "projective simulation applied to the grid world and the mountain car problem"
    - "active learning with statistical models"

Connected through hyperedge HE-5 (induced by source paper "meta learning within projective simulation"):
  with members:
    - "hierarchical haptic manipulation for complex skill learning"
    - "projective simulation applied to the grid world and the mountain car problem"
    - "recommending learning algorithms and their associated hyperparameters"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "bandit problems with side observations"
    - "learning to learn"

Question: Please tell me which class the center node 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)