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: "learning to coordinate efficiently a model based approach"
  Abstract: In common-interest stochastic games all players receive an identical payoff. Players participating in such games must learn to coordinate with each other in order to receive the highest-possible value. A number of reinforcement learning algorithms have been proposed for this problem, and some have been shown to converge to good solutions in the limit. In this paper we show that using very simple model-based algorithms, much better (i.e., polynomial) convergence rates can be attained. Moreover, our model-based algorithms are guaranteed to converge to the optimal value, unlike many of the existing algorithms.

Connected through hyperedge HE-1 (induced by source paper "multi agent common knowledge reinforcement learning"):
  with members:
    - "the starcraft multi agent challenge"
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning to communicate implicitly by actions"
    - "cooperative and distributed reinforcement learning of drones for field coverage"
    - "multi vehicle flocking control with deep deterministic policy gradient method"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "agreeing to cross how drivers and pedestrians communicate"

Connected through hyperedge HE-2 (induced by source paper "replanning in domains with partial information and sensing actions"):
  with members:
    - "heuristic search value iteration for pomdps"
    - "compiling uncertainty away in conformant planning problems with bounded width"
    - "planning graph heuristics for belief space search"
    - "the ff planning system fast plan generation through heuristic search"
    - "conformant planning via symbolic model checking"

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)