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 bayesian model for plan recognition in rts games applied to starcraft"
  Abstract: The task of keyhole (unobtrusive) plan recognition is central to adaptive game AI. "Tech trees" or "build trees" are the core of real-time strategy (RTS) game strategic (long term) planning. This paper presents a generic and simple Bayesian model for RTS build tree prediction from noisy observations, which parameters are learned from replays (game logs). This unsupervised machine learning approach involves minimal work for the game developers as it leverage players' data (com- mon in RTS). We applied it to StarCraft1 and showed that it yields high quality and robust predictions, that can feed an adaptive AI.

Connected through hyperedge HE-1 (induced by source paper "time to die death prediction in dota 2 using deep learning"):
  with members:
    - "win prediction in esports mixed rank match prediction in multi player online battle arena 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 dataset for starcraft ai an example of armies clustering"

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"
    - "a dataset for starcraft ai an example of armies clustering"
    - "inferring strategies from limited reconnaissance in real time strategy games"

Connected through hyperedge HE-5 (induced by source paper "combining strategic learning and tactical search in real time strategy games"):
  with members:
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "episodic exploration for deep deterministic policies an application to starcraft micromanagement tasks"
    - "learning multiagent communication with backpropagation"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"
    - "striving for simplicity the all convolutional net"

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.LG(Machine Learning)