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 "from few to more large scale dynamic multiagent curriculum learning"):
  Abstract: A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale multiagent settings due to the complex dynamics between the environment and agents and the explosion of state-action space. In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents. We propose three transfer mechanisms across curricula to accelerate the learning process. Moreover, due to the fact that the state dimension varies across curricula,, and existing network structures cannot be applied in such a transfer setting since their network input sizes are fixed. Therefore, we design a novel network structure called Dynamic Agent-number Network (DyAN) to handle the dynamic size of the network input. Experimental results show that DyMA-CL using DyAN greatly improves the performance of large-scale multiagent learning compared with state-of-the-art deep reinforcement learning approaches. We also investigate the influence of three transfer mechanisms across curricula through extensive simulations.

Includes members:
  Member "the starcraft multi agent challenge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "learning transferable cooperative behavior in multi agent teams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "connectivity driven communication in multi agent reinforcement learning through diffusion processes on graphs")
  Member "qmix monotonic value function factorisation for deep multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "learning attentional communication for multi agent cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")
  Member "factorized q learning for large scale multi agent systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi agent game abstraction via graph attention neural network")
  Member "learning curriculum policies for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey")
  Member "how powerful are graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "deep q learning from demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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.AI(Artificial Intelligence)