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 "strategy synthesis for multi dimensional quantitative objectives"):
  Abstract: Multi-dimensional mean-payoff and energy games provide the mathematical foundation for the quantitative study of reactive systems, and play a central role in the emerging quantitative theory of verification and synthesis. In this work, we study the strategy synthesis problem for games with such multi-dimensional objectives along with a parity condition, a canonical way to express $\omega$-regular conditions. While in general, the winning strategies in such games may require infinite memory, for synthesis the most relevant problem is the construction of a finite-memory winning strategy (if one exists). Our main contributions are as follows. First, we show a tight exponential bound (matching upper and lower bounds) on the memory required for finite-memory winning strategies in both multi-dimensional mean-payoff and energy games along with parity objectives. This significantly improves the triple exponential upper bound for multi energy games (without parity) that could be derived from results in literature for games on VASS (vector addition systems with states). Second, we present an optimal symbolic and incremental algorithm to compute a finite-memory winning strategy (if one exists) in such games. Finally, we give a complete characterization of when finite memory of strategies can be traded off for randomness. In particular, we show that for one-dimension mean-payoff parity games, randomized memoryless strategies are as powerful as their pure finite-memory counterparts.

Includes members:
  Member "looking at mean payoff and total payoff through windows":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "games where you can play optimally with finite memory")
  Member "meet your expectations with guarantees beyond worst case synthesis in quantitative games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "games where you can play optimally with finite memory")
  Member "synthesis from ltl specifications with mean payoff objectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perfect information stochastic games with generalized mean payoff objectives")
  Member "measuring permissiveness in parity games mean payoff parity games revisited":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quantitative reductions and vertex ranked infinite games")
  Member "reachability games on extended vector addition systems with states":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "energy mean payoff games")
  Member "generalized mean payoff and energy games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deciding fast termination for probabilistic vass with nondeterminism")
  Member "energy parity games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "games where you can play optimally with finite memory")
  Member "better quality in synthesis through quantitative objectives":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "imitation projected programmatic reinforcement learning")

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)