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: "approximation of lorenz optimal solutions in multiobjective markov decision processes"
  Abstract: This paper is devoted to fair optimization in Multiobjective Markov Decision Processes (MOMDPs). A MOMDP is an extension of the MDP model for planning under uncertainty while trying to optimize several reward functions simultaneously. This applies to multiagent problems when rewards define individual utility functions, or in multicriteria problems when rewards refer to different features. In this setting, we study the determination of policies leading to Lorenz-non-dominated tradeoffs. Lorenz dominance is a refinement of Pareto dominance that was introduced in Social Choice for the measurement of inequalities. In this paper, we introduce methods to efficiently approximate the sets of Lorenz-non-dominated solutions of infinite-horizon, discounted MOMDPs. The approximations are polynomial-sized subsets of those solutions.

Connected through hyperedge HE-1 (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-2 (induced by source paper "fairness in reinforcement learning"):
  with members:
    - "an efficient primal dual algorithm for fair combinatorial optimization problems"
    - "a reductions approach to fair classification"
    - "fairness behind a veil of ignorance a welfare analysis for automated decision making"
    - "optimal threshold policies for robust data center control"
    - "multi objective bandits optimizing the generalized gini index"
    - "a survey of multi objective sequential decision making"

Connected through hyperedge HE-3 (induced by source paper "multi objective robust strategy synthesis for interval markov decision processes"):
  with members:
    - "percentile queries in multi dimensional markov decision processes"
    - "pareto curves for probabilistic model checking"
    - "multi objective model checking of markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "optimal threshold policies for robust data center control"):
  with members:
    - "on the complexity of solving markov decision problems"

Connected through hyperedge HE-5 (induced by source paper "from preference based to multiobjective sequential decision making"):
  with members:
    - "contextual dueling bandits"
    - "a survey of multi objective sequential decision making"
    - "april active preference learning based reinforcement learning"
    - "implementation and comparison of solution methods for decision processes with non markovian rewards"

Connected through hyperedge HE-6 (induced by source paper "a survey of multi objective sequential decision making"):
  with members:
    - "markov decision processes with multiple long run averageobjectives"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "a theory of goal oriented mdps with dead ends"
    - "pareto curves for probabilistic model checking"
    - "online planning algorithms for pomdps"
    - "risk sensitive reinforcement learning applied to control under constraints"
    - "perseus randomized point based value iteration for pomdps"
    - "an evolutionary algorithm with advanced goal and priority specification for multi objective optimization"

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)