human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "policy gradient with value function approximation for collective multiagent planning"
  Abstract: Decentralized (PO)MDPs provide an expressive framework for sequential decision making in a multiagent system. Given their computational complexity, recent research has focused on tractable yet practical subclasses of Dec-POMDPs. We address such a subclass called CDEC-POMDP where the collective behavior of a population of agents affects the joint-reward and environment dynamics. Our main contribution is an actor-critic (AC) reinforcement learning method for optimizing CDEC-POMDP policies. Vanilla AC has slow convergence for larger problems. To address this, we show how a particular decomposition of the approximate action-value function over agents leads to effective updates, and also derive a new way to train the critic based on local reward signals. Comparisons on a synthetic benchmark and a real-world taxi fleet optimization problem show that our new AC approach provides better quality solutions than previous best approaches.

Connected through hyperedge HE-1 (induced by source paper "context aware deep q network for decentralized cooperative reconnaissance by a robotic swarm"):
  with members:
    - "review of multi agent algorithms for collective behavior a structural taxonomy"
    - "learning for multi robot cooperation in partially observable stochastic environments with macro actions"
    - "fully decentralized policies for multi agent systems an information theoretic approach"

Connected through hyperedge HE-2 (induced by source paper "entropy based independent learning in anonymous multi agent settings"):
  with members:
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "stabilising experience replay for deep multi agent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "efficient collaborative multi agent deep reinforcement learning for large scale fleet management"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "magent a many agent reinforcement learning platform for artificial collective intelligence"
    - "deep reinforcement learning a brief survey"
    - "optlayer practical constrained optimization for deep reinforcement learning in the real world"
    - "value decomposition networks for cooperative multi agent learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-4 (induced by source paper "actor critic policy optimization in partially observable multiagent environments"):
  with members:
    - "combining no regret and q learning"
    - "introduction to online convex optimization"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "a generalised method for empirical game theoretic analysis"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "emergent communication through negotiation"

Connected through hyperedge HE-5 (induced by source paper "efficient large scale fleet management via multi agent deep reinforcement learning"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "magent a many agent reinforcement learning platform for artificial collective intelligence"
    - "deep reinforcement learning a brief survey"
    - "optlayer practical constrained optimization for deep reinforcement learning in the real world"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "identity mappings in deep residual networks"
    - "asynchronous methods for deep reinforcement learning"
    - "multiagent cooperation and competition with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "maximum entropy based independent learning in anonymous multi agent settings"):
  with members:
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "a survey of learning in multiagent environments dealing with non stationarity"
    - "asynchronous methods for deep reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "deep reinforcement learning in parameterized action space"

Question: Please tell me which class the center node 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)