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 "maven multi agent variational exploration"):
  Abstract: Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution and computational tractability in training. In this paper, we analyse value-based methods that are known to have superior performance in complex environments [43]. We specifically focus on QMIX [40], the current state-of-the-art in this domain. We show that the representational constraints on the joint action-values introduced by QMIX and similar methods lead to provably poor exploration and suboptimality. Furthermore, we propose a novel approach called MAVEN that hybridises value and policy-based methods by introducing a latent space for hierarchical control. The value-based agents condition their behaviour on the shared latent variable controlled by a hierarchical policy. This allows MAVEN to achieve committed, temporally extended exploration, which is key to solving complex multi-agent tasks. Our experimental results show that MAVEN achieves significant performance improvements on the challenging SMAC domain [43].

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 "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "cesma centralized expert supervises multi agents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "qmix monotonic value function factorisation for deep multi agent reinforcement learning")
  Member "qmix monotonic value function factorisation for deep multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")
  Member "virel a variational inference framework for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "making sense of reinforcement learning and probabilistic inference")
  Member "new and surprising ways to be mean adversarial npcs with coupled empowerment minimisation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "social influence as intrinsic motivation for multi agent deep reinforcement learning")
  Member "coordinated exploration in concurrent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "population guided parallel policy search for reinforcement 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)