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 "multidimensional beyond worst case and almost sure problems for mean payoff objectives"):
  Abstract: The beyond worst-case threshold problem (BWC), recently introduced by Bruy\`ere et al., asks given a quantitative game graph for the synthesis of a strategy that i) enforces some minimal level of performance against any adversary, and ii) achieves a good expectation against a stochastic model of the adversary. They solved the BWC problem for finite-memory strategies and unidimensional mean-payoff objectives and they showed membership of the problem in NP$\cap$coNP. They also noted that infinite-memory strategies are more powerful than finite-memory ones, but the respective threshold problem was left open. We extend these results in several directions. First, we consider multidimensional mean-payoff objectives. Second, we study both finite-memory and infinite-memory strategies. We show that the multidimensional BWC problem is coNP-complete in both cases. Third, in the special case when the worst-case objective is unidimensional (but the expectation objective is still multidimensional) we show that the complexity decreases to NP$\cap$coNP. This solves the infinite-memory threshold problem left open by Bruy\`ere et al., and this complexity cannot be improved without improving the currently known complexity of classical mean-payoff games. Finally, we introduce a natural relaxation of the BWC problem, the beyond almost-sure threshold problem (BAS), which asks for the synthesis of a strategy that ensures some minimal level of performance with probability one and a good expectation against the stochastic model of the adversary. We show that the multidimensional BAS threshold problem is solvable in P.

Includes members:
  Member "unifying two views on multiple mean payoff objectives in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi weighted markov decision processes with reachability objectives")
  Member "percentile queries in multi dimensional markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "life is random time is not markov decision processes with window objectives")
  Member "markov decision processes with multiple long run averageobjectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deciding fast termination for probabilistic vass with nondeterminism")
  Member "meet your expectations with guarantees beyond worst case synthesis in quantitative games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "games where you can play optimally with finite memory")
  Member "the complexity of multi mean payoff and multi energy games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "games where you can play optimally with finite memory")
  Member "hyperplane separation technique for multidimensional mean payoff games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deciding fast termination for probabilistic vass with nondeterminism")

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.GT(Computer Science and Game Theory)