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 "voting based multi agent reinforcement learning"):
  Abstract: The recent success of single-agent reinforcement learning (RL) encourages the exploration of multi-agent reinforcement learning (MARL), which is more challenging due to the interactions among different agents. In this paper, we consider a voting-based MARL problem, in which the agents vote to make group decisions and the goal is to maximize the globally averaged returns. To this end, we formulate the MARL problem based on the linear programming form of the policy optimization problem and propose a distributed primal-dual algorithm to obtain the optimal solution. We also propose a voting mechanism through which the distributed learning achieves the same sub-linear convergence rate as centralized learning. In other words, the distributed decision making does not slow down the global consensus to optimal. We also verify the convergence of our proposed algorithm with numerical simulations and conduct case studies in practical multi-agent systems.

Includes members:
  Member "mean field multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scaling up multiagent reinforcement learning for robotic systems learn an adaptive sparse communication graph")
  Member "scalable bilinear pi learning using state and action features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning via fenchel rockafellar duality")
  Member "a tutorial on uavs for wireless networks applications challenges and open problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "multi agent reinforcement learning via double averaging primal dual optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems")
  Member "applications of deep learning and reinforcement learning to biological data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "3d aggregated faster r cnn for general lesion detection")
  Member "fully decentralized multi agent reinforcement learning with networked agents":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cluster based social reinforcement learning")
  Member "placement optimization of uav mounted mobile base stations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "analysis of uav communications in cell free massive mimo systems")
  Member "stabilising experience replay for deep 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)