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: "hierarchical macro strategy model for moba game ai"
  Abstract: The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an action space much larger than that of GO. Mastering RTS games requires both strong macro strategies and delicate micro level execution. Recently, great progress has been made in micro level execution, while complete solutions for macro strategies are still lacking. In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games. Trained by the Hierarchical Macro Strategy model, agents explicitly make macro strategy decisions and further guide their micro level execution. Moreover, each of the agents makes independent strategy decisions, while simultaneously communicating with the allies through leveraging a novel imitated cross-agent communication mechanism. We perform comprehensive evaluations on a popular 5v5 Multiplayer Online Battle Arena (MOBA) game. Our 5-AI team achieves a 48% winning rate against human player teams which are ranked top 1% in the player ranking system.

Connected through hyperedge HE-1 (induced by source paper "hierarchical decision making by generating and following natural language instructions"):
  with members:
    - "relational deep reinforcement learning"
    - "speaker follower models for vision and language navigation"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "unified pragmatic models for generating and following instructions"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "starcraft ii a new challenge for reinforcement learning"
    - "parlai a dialog research software platform"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "mastering complex control in moba games with deep reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "marginal policy gradients a unified family of estimators for bounded action spaces with applications"
    - "an empirical model of large batch training"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "feedback based tree search for reinforcement learning"
    - "moba a new arena for game ai"
    - "emergent complexity via multi agent competition"

Connected through hyperedge HE-3 (induced by source paper "efficient reinforcement learning with a thought game for starcraft"):
  with members:
    - "accuracy based curriculum learning in deep reinforcement learning"
    - "relational deep reinforcement learning"
    - "macro action selection with deep reinforcement learning in starcraft"
    - "world models"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "on reinforcement learning for full length game of starcraft"
    - "modular architecture for starcraft ii with deep reinforcement learning"
    - "tunneling neural perception and logic reasoning through abductive learning"

Connected through hyperedge HE-4 (induced by source paper "hierarchical reinforcement learning for multi agent moba game"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "episodic memory deep q networks"
    - "self imitation learning"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "meta learning shared hierarchies"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "reward shaping via meta learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learning to reinforcement learn"
    - "matching networks for one shot learning"
    - "value iteration networks"
    - "learning to act by predicting the future"

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.MA(Multiagent Systems)