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: "using strategy improvement to stay alive"
  Abstract: We design a novel algorithm for solving Mean-Payoff Games (MPGs). Besides solving an MPG in the usual sense, our algorithm computes more information about the game, information that is important with respect to applications. The weights of the edges of an MPG can be thought of as a gained/consumed energy -- depending on the sign. For each vertex, our algorithm computes the minimum amount of initial energy that is sufficient for player Max to ensure that in a play starting from the vertex, the energy level never goes below zero. Our algorithm is not the first algorithm that computes the minimum sufficient initial energies, but according to our experimental study it is the fastest algorithm that computes them. The reason is that it utilizes the strategy improvement technique which is very efficient in practice.

Connected through hyperedge HE-1 (induced by source paper "solving mean payoff games via quasi dominions"):
  with members:
    - "oink an implementation and evaluation of modern parity game solvers"
    - "succinct progress measures for solving parity games"
    - "dynamic consistency of conditional simple temporal networks via mean payoff games a singly exponential time dc checking"

Connected through hyperedge HE-2 (induced by source paper "efficient strategy iteration for mean payoff in markov decision processes"):
  with members:
    - "value iteration for long run average reward in markov decision processes"
    - "efficient parallel strategy improvement for parity games"
    - "synthesising strategy improvement and recursive algorithms for solving 2 5 player parity games"
    - "multigain a controller synthesis tool for mdps with multiple mean payoff objectives"
    - "verification of markov decision processes using learning algorithms"
    - "parametric strategy iteration"
    - "assume guarantee abstraction refinement for probabilistic systems"
    - "the complexity of solving reachability games using value and strategy iteration"

Connected through hyperedge HE-3 (induced by source paper "checking dynamic consistency of conditional hyper temporal networks via mean payoff games hardness and pseudo singly exponential time algorithm"):
  with members:
    - "hyper temporal networks"
    - "polynomial time algorithms for energy games with special weight structures"
    - "the tropical shadow vertex algorithm solves mean payoff games in polynomial time on average"
    - "the dynamic controllability of conditional stns with uncertainty"

Connected through hyperedge HE-4 (induced by source paper "dynamic consistency of conditional simple temporal networks via mean payoff games a singly exponential time dc checking"):
  with members:
    - "hyper temporal networks"
    - "polynomial time algorithms for energy games with special weight structures"
    - "the tropical shadow vertex algorithm solves mean payoff games in polynomial time on average"
    - "the dynamic controllability of conditional stns with uncertainty"

Connected through hyperedge HE-5 (induced by source paper "checking dynamic consistency of conditional simple temporal networks via mean payoff games a singly exponential time algorithm"):
  with members:
    - "hyper temporal networks"
    - "polynomial time algorithms for energy games with special weight structures"
    - "the dynamic controllability of conditional stns with uncertainty"

Connected through hyperedge HE-6 (induced by source paper "hyper temporal networks"):
  with members:
    - "symmetric strategy improvement"
    - "dynamic consistency of conditional simple temporal networks via mean payoff games a singly exponential time dc checking"

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.GT(Computer Science and Game Theory)