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 "hierarchical critics assignment for multi agent reinforcement learning"):
  Abstract: In this paper, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of multi-agent reinforcement learning (MARL) tasks. Within the actor-critic MARL, we introduce multiple cooperative critics from two levels of the hierarchy and propose a hierarchical critic-based MARL algorithm. In our approach, the agent is allowed to receive information from local and global critics in a competition task. The agent not only receives low-level details but also considers coordination from high levels to obtain global information for increasing operational performance. Here, we define multiple cooperative critics in a top-down hierarchy, called the Hierarchical Critic Assignment (HCA) framework. Our experiment, a two-player tennis competition task performed in the Unity environment, tested the HCA multi-agent framework based on the Asynchronous Advantage Actor-Critic (A3C) with Proximal Policy Optimization (PPO) algorithm. The results showed that the HCA framework outperforms the non-hierarchical critic baseline method on MARL tasks.

Includes members:
  Member "the representational capacity of action value networks for multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep coordination graphs")
  Member "feudal multi agent hierarchies for cooperative reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")
  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 "starcraft micromanagement with reinforcement learning and curriculum transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey")
  Member "data efficient hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation")
  Member "deep reinforcement learning for multi agent systems a review of challenges solutions and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "unity a general platform for intelligent agents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "meta learning shared hierarchies":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting meta learning as supervised learning")

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)