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: "optimal and approximate q value functions for decentralized pomdps"
  Abstract: Decision-theoretic planning is a popular approach to sequential decision making problems, because it treats uncertainty in sensing and acting in a principled way. In single-agent frameworks like MDPs and POMDPs, planning can be carried out by resorting to Q-value functions: an optimal Q-value function Q* is computed in a recursive manner by dynamic programming, and then an optimal policy is extracted from Q*. In this paper we study whether similar Q-value functions can be defined for decentralized POMDP models (Dec-POMDPs), and how policies can be extracted from such value functions. We define two forms of the optimal Q-value function for Dec-POMDPs: one that gives a normative description as the Q-value function of an optimal pure joint policy and another one that is sequentially rational and thus gives a recipe for computation. This computation, however, is infeasible for all but the smallest problems. Therefore, we analyze various approximate Q-value functions that allow for efficient computation. We describe how they relate, and we prove that they all provide an upper bound to the optimal Q-value function Q*. Finally, unifying some previous approaches for solving Dec-POMDPs, we describe a family of algorithms for extracting policies from such Q-value functions, and perform an experimental evaluation on existing test problems, including a new firefighting benchmark problem.

Connected through hyperedge HE-1 (induced by source paper "deep multi agent reinforcement learning for decentralized continuous cooperative control"):
  with members:
    - "q learning in enormous action spaces via amortized approximate maximization"
    - "r maddpg for partially observable environments and limited communication"
    - "the starcraft multi agent challenge"
    - "deep coordination graphs"
    - "reducing overestimation bias in multi agent domains using double centralized critics"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "coordination in adversarial sequential team games via multi agent deep reinforcement learning"):
  with members:
    - "signal instructed coordination in cooperative multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "signal instructed coordination in team competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "deep counterfactual regret minimization"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-3 (induced by source paper "multi objective multi agent decision making a utility based analysis and survey"):
  with members:
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "resource abstraction for reinforcement learning in multiagent congestion problems"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "dynamic weights in multi objective deep reinforcement learning"
    - "ordered preference elicitation strategies for supporting multi objective decision making"

Connected through hyperedge HE-4 (induced by source paper "signal instructed coordination in cooperative multi agent reinforcement learning"):
  with members:
    - "on the pitfalls of measuring emergent communication"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "correlation in extensive form games saddle point formulation and benchmarks"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "the starcraft multi agent challenge"):
  with members:
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "multi agent common knowledge reinforcement learning"
    - "value propagation networks"
    - "mean field multi agent reinforcement learning"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "on reinforcement learning for full length game of starcraft"

Connected through hyperedge HE-6 (induced by source paper "cesma centralized expert supervises multi agents"):
  with members:
    - "decentralization of multiagent policies by learning what to communicate"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "action branching architectures for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "smix lambda enhancing centralized value functions for cooperative multi agent reinforcement learning"):
  with members:
    - "understanding multi step deep reinforcement learning a systematic study of the dqn target"
    - "the starcraft multi agent challenge"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "off policy deep reinforcement learning without exploration"

Connected through hyperedge HE-8 (induced by source paper "improving policies via search in cooperative partially observable games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "rethinking formal models of partially observable multiagent decision making"
    - "simplified action decoder for deep multi agent reinforcement learning"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "distributed prioritized experience replay"
    - "evaluating and modelling hanabi playing agents"

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)