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: "a dataset for starcraft ai an example of armies clustering"
  Abstract: This paper advocates the exploration of the full state of recorded real-time strategy (RTS) games, by human or robotic players, to discover how to reason about tactics and strategy. We present a dataset of StarCraft games encompassing the most of the games' state (not only player's orders). We explain one of the possible usages of this dataset by clustering armies on their compositions. This reduction of armies compositions to mixtures of Gaussian allow for strategic reasoning at the level of the components. We evaluated this clustering method by predicting the outcomes of battles based on armies compositions' mixtures components

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"
    - "inferring strategies from limited reconnaissance in real time strategy games"

Connected through hyperedge HE-2 (induced by source paper "msc a dataset for macro management in starcraft ii"):
  with members:
    - "learning macromanagement in starcraft from replays using deep learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "stardata a starcraft ai research dataset"
    - "asynchronous methods for deep reinforcement learning"
    - "playing fps games with deep reinforcement learning"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-3 (induced by source paper "learning macromanagement in starcraft from replays using deep learning"):
  with members:
    - "the game imitation deep supervised convolutional networks for quick video game ai"
    - "asynchronous methods for deep reinforcement learning"
    - "episodic exploration for deep deterministic policies an application to starcraft micromanagement tasks"
    - "playing fps games with deep reinforcement learning"
    - "adam a method for stochastic optimization"
    - "deep speech scaling up end to end speech recognition"
    - "neuroevolution in games state of the art and open challenges"
    - "a bayesian model for plan recognition in rts games applied to starcraft"

Connected through hyperedge HE-4 (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"
    - "inferring strategies from limited reconnaissance in real time strategy games"
    - "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)