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: "optimizing the expected mean payoff in energy markov decision processes"
  Abstract: Energy Markov Decision Processes (EMDPs) are finite-state Markov decision processes where each transition is assigned an integer counter update and a rational payoff. An EMDP configuration is a pair s(n), where s is a control state and n is the current counter value. The configurations are changed by performing transitions in the standard way. We consider the problem of computing a safe strategy (i.e., a strategy that keeps the counter non-negative) which maximizes the expected mean payoff.

Connected through hyperedge HE-1 (induced by source paper "energy mean payoff games"):
  with members:
    - "perfect half space games"
    - "computer aided synthesis a game theoretic approach"
    - "on the complexity of heterogeneous multidimensional quantitative games"
    - "average energy games"
    - "fixed dimensional energy games are in pseudo polynomial time"
    - "infinite state energy games"
    - "robust multidimensional mean payoff games are undecidable"
    - "solving parity games on integer vectors"

Connected through hyperedge HE-2 (induced by source paper "stochastic shortest paths and weight bounded properties in markov decision processes"):
  with members:
    - "mdps with energy parity objectives"
    - "qualitative analysis of vass induced mdps"
    - "variations on the stochastic shortest path problem"
    - "the odds of staying on budget"
    - "computing quantiles in markov reward models"
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"
    - "energy and mean payoff parity markov decision processes"
    - "energy parity games"

Connected through hyperedge HE-3 (induced by source paper "learning based mean payoff optimization in an unknown mdp under omega regular constraints"):
  with members:
    - "safe reinforcement learning via shielding"
    - "correct by synthesis reinforcement learning with temporal logic constraints"
    - "first cycle games"

Connected through hyperedge HE-4 (induced by source paper "mdps with energy parity objectives"):
  with members:
    - "qualitative analysis of vass induced mdps"
    - "solving parity games on integer vectors"
    - "energy and mean payoff parity markov decision processes"
    - "energy parity games"
    - "better quality in synthesis through quantitative objectives"

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.LO(Logic in Computer Science)