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 applied to the grid world and the mountain car problem"
  Abstract: We study the model of projective simulation (PS) which is a novel approach to artificial intelligence (AI). Recently it was shown that the PS agent performs well in a number of simple task environments, also when compared to standard models of reinforcement learning (RL). In this paper we study the performance of the PS agent further in more complicated scenarios. To that end we chose two well-studied benchmarking problems, namely the "grid-world" and the "mountain-car" problem, which challenge the model with large and continuous input space. We compare the performance of the PS agent model with those of existing models and show that the PS agent exhibits competitive performance also in such scenarios.

Connected through hyperedge HE-1 (induced by source paper "aerial animal biometrics individual friesian cattle recovery and visual identification via an autonomous uav with onboard deep inference"):
  with members:
    - "yolo9000 better faster stronger"
    - "why should i trust you explaining the predictions of any classifier"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "benchmarking projective simulation in navigation problems"):
  with members:
    - "projective simulation with generalization"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "projective simulation with generalization"):
  with members:
    - "meta learning within projective simulation"
    - "robotic playing for hierarchical complex skill learning"
    - "feature reinforcement learning in practice"
    - "feature reinforcement learning part i unstructured mdps"
    - "bandit problems with side observations"

Connected through hyperedge HE-4 (induced by source paper "skill learning by autonomous robotic playing using active learning and creativity"):
  with members:
    - "projective simulation with generalization"
    - "robotic playing for hierarchical complex skill learning"
    - "active learning with statistical models"

Connected through hyperedge HE-5 (induced by source paper "meta learning within projective simulation"):
  with members:
    - "projective simulation with generalization"
    - "hierarchical haptic manipulation for complex skill learning"
    - "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)