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: "inferring strategies from limited reconnaissance in real time strategy games"
  Abstract: In typical real-time strategy (RTS) games, enemy units are visible only when they are within sight range of a friendly unit. Knowledge of an opponent's disposition is limited to what can be observed through scouting. Information is costly, since units dedicated to scouting are unavailable for other purposes, and the enemy will resist scouting attempts. It is important to infer as much as possible about the opponent's current and future strategy from the available observations. We present a dynamic Bayes net model of strategies in the RTS game Starcraft that combines a generative model of how strategies relate to observable quantities with a principled framework for incorporating evidence gained via scouting. We demonstrate the model's ability to infer unobserved aspects of the game from realistic observations.

Connected through hyperedge HE-1 (induced by source paper "macro action selection with deep reinforcement learning in starcraft"):
  with members:
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "distributed prioritized experience replay"
    - "revisiting the master slave architecture in multi agent deep reinforcement learning"
    - "torchcraft a library for machine learning research on real time strategy games"
    - "deep recurrent q learning for partially observable mdps"
    - "a dataset for starcraft ai an example of armies clustering"

Connected through hyperedge HE-2 (induced by source paper "stardata a starcraft ai research dataset"):
  with members:
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "torchcraft a library for machine learning research on real time strategy games"
    - "episodic exploration for deep deterministic policies an application to starcraft micromanagement tasks"
    - "deep multi scale video prediction beyond mean square error"
    - "a dataset for starcraft ai an example of armies clustering"
    - "a bayesian model for plan recognition in rts games applied to starcraft"

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)