human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient reinforcement learning with a thought game for starcraft"):
  Abstract: StarCraft provides an extremely challenging platform for reinforcement learning due to its huge state-space and game length. The previous fastest method requires days to train a full-length game policy in a single commercial machine. Introduction of background knowledge can accelerate the training of reinforcement learning. But how to effectively introduce background knowledge is still an open question. In this paper, we incorporate the background knowledge to reinforcement learning in the form of a thought-game. With the thought-game, the policy is firstly trained in the thought-game fastly and is then transferred to the real game using mapping functions for the second phase training. In our experiments, the trained agent can achieve a 100\% win-rate on the map \textit{Simple64} against the most difficult non-cheating built-in bot (level-7), and the training is 100 times faster than the previous ones under the same computational resource. To test the generalization performance of the agent, a Golden level of StarCraft~II Ladder human player has competed with the agent. With restricted strategy, the agent wins the human player by 4 out of 5 games. We also apply thought-game idea to another game which is "StarCraft: Brood War", the predecessor of StarCraft II. The thought-game approach might shed some light for further studies of efficient reinforcement learning.

Includes members:
  Member "accuracy based curriculum learning in deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic curriculum learning for deep rl a short survey")
  Member "relational deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "macro action selection with deep reinforcement learning in starcraft":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "defoggan predicting hidden information in the starcraft fog of war with generative adversarial nets")
  Member "world models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "hierarchical macro strategy model for moba game ai":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical decision making by generating and following natural language instructions")
  Member "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for cyber security")
  Member "on reinforcement learning for full length game of starcraft":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for cyber security")
  Member "modular architecture for starcraft ii with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "why build an assistant in minecraft")

Question: Please tell me which class the source paper 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)