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 "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication"):
  Abstract: Deep reinforcement learning algorithms have recently been used to train multiple interacting agents in a centralised manner whilst keeping their execution decentralised. When the agents can only acquire partial observations and are faced with tasks requiring coordination and synchronisation skills, inter-agent communication plays an essential role. In this work, we propose a framework for multi-agent training using deep deterministic policy gradients that enables concurrent, end-to-end learning of an explicit communication protocol through a memory device. During training, the agents learn to perform read and write operations enabling them to infer a shared representation of the world. We empirically demonstrate that concurrent learning of the communication device and individual policies can improve inter-agent coordination and performance in small-scale systems. Our experimental results show that the proposed method achieves superior performance in scenarios with up to six agents. We illustrate how different communication patterns can emerge on six different tasks of increasing complexity. Furthermore, we study the effects of corrupting the communication channel, provide a visualisation of the time-varying memory content as the underlying task is being solved and validate the building blocks of the proposed memory device through ablation studies.

Includes members:
  Member "learning to schedule communication in multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "feudal multi agent hierarchies for cooperative reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning from hierarchical critics")
  Member "probabilistic recursive reasoning for 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 "multi agent reinforcement learning with emergent roles")
  Member "bayesian action decoder for deep multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "tarmac targeted multi agent communication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "learning when to communicate at scale in multiagent cooperative and competitive tasks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "actor attention critic for multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi agent reinforcement learning with emergent roles")

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)