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: "the complexity of interior point methods for solving discounted turn based stochastic games"
  Abstract: We study the problem of solving discounted, two player, turn based, stochastic games (2TBSGs). Jurdzinski and Savani showed that 2TBSGs with deterministic transitions can be reduced to solving $P$-matrix linear complementarity problems (LCPs). We show that the same reduction works for general 2TBSGs. This implies that a number of interior point methods for solving $P$-matrix LCPs can be used to solve 2TBSGs. We consider two such algorithms. First, we consider the unified interior point method of Kojima, Megiddo, Noma, and Yoshise, which runs in time $O((1+\kappa)n^{3.5}L)$, where $\kappa$ is a parameter that depends on the $n \times n$ matrix $M$ defining the LCP, and $L$ is the number of bits in the representation of $M$. Second, we consider the interior point potential reduction algorithm of Kojima, Megiddo, and Ye, which runs in time $O(\frac{-\delta}{\theta}n^4\log \epsilon^{-1})$, where $\delta$ and $\theta$ are parameters that depend on $M$, and $\epsilon$ describes the quality of the solution. For 2TBSGs with $n$ states and discount factor $\gamma$ we prove that in the worst case $\kappa = \Theta(n/(1-\gamma)^2)$, $-\delta = \Theta(\sqrt{n}/(1-\gamma))$, and $1/\theta = \Theta(n/(1-\gamma)^2)$. The lower bounds for $\kappa$, $-\delta$, and $1/\theta$ are obtained using the same family of deterministic games.

Connected through hyperedge HE-1 (induced by source paper "unique end of potential line"):
  with members:
    - "end of potential line"
    - "arrival next stop in cls"
    - "cls new problems and completeness"
    - "an ordered approach to solving parity games in quasi polynomial time and quasi linear space"
    - "succinct progress measures for solving parity games"
    - "the complexity of all switches strategy improvement"
    - "constant rank bimatrix games are ppad hard"
    - "settling the complexity of computing two player nash equilibria"

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)