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 simplex method is strongly polynomial for deterministic markov decision processes"
  Abstract: We prove that the simplex method with the highest gain/most-negative-reduced cost pivoting rule converges in strongly polynomial time for deterministic Markov decision processes (MDPs) regardless of the discount factor. For a deterministic MDP with n states and m actions, we prove the simplex method runs in O(n^3m^2log^2 n) iterations if the discount factor is uniform and O(n^5m^3log^2 n) iterations if each action has a distinct discount factor. Previously the simplex method was known to run in polynomial time only for discounted MDPs where the discount was bounded away from 1 [Ye11]. #R##N#Unlike in the discounted case, the algorithm does not greedily converge to the optimum, and we require a more complex measure of progress. We identify a set of layers in which the values of primal variables must lie and show that the simplex method always makes progress optimizing one layer, and when the upper layer is updated the algorithm makes a substantial amount of progress. In the case of nonuniform discounts, we define a polynomial number of "milestone" policies and we prove that, while the objective function may not improve substantially overall, the value of at least one dual variable is always making progress towards some milestone, and the algorithm will reach the next milestone in a polynomial number of steps.

Connected through hyperedge HE-1 (induced by source paper "toward solving 2 tbsg efficiently"):
  with members:
    - "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor"
    - "exponential lower bounds for policy iteration"

Connected through hyperedge HE-2 (induced by source paper "a friendly smoothed analysis of the simplex method"):
  with members:
    - "smoothed analysis of the successive shortest path algorithm"
    - "on the shadow simplex method for curved polyhedra"
    - "geometric random edge"
    - "solving totally unimodular lps with the shadow vertex algorithm"
    - "beyond hirsch conjecture walks on random polytopes and smoothed complexity of the simplex method"
    - "smoothed analysis of the condition numbers and growth factors of matrices"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-3 (induced by source paper "improved bound on the worst case complexity of policy iteration"):
  with members:
    - "a complexity analysis of policy iteration through combinatorial matrices arising from unique sink orientations"
    - "pagerank optimization by edge selection"
    - "the value iteration algorithm is not strongly polynomial for discounted dynamic programming"
    - "on the complexity of policy iteration"
    - "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor"
    - "exponential lower bounds for policy iteration"

Connected through hyperedge HE-4 (induced by source paper "the complexity of the simplex method"):
  with members:
    - "on simplex pivoting rules and complexity theory"
    - "an exponential lower bound for the latest deterministic strategy iteration algorithms"
    - "the complexity of the homotopy method equilibrium selection and lemke howson solutions"
    - "exponential lower bounds for policy iteration"
    - "settling the complexity of computing two player nash equilibria"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-5 (induced by source paper "an exponential lower bound for cunningham s rule"):
  with members:
    - "an exponential lower bound for the latest deterministic strategy iteration algorithms"
    - "from coinductive proofs to exact real arithmetic theory and applications"
    - "on the existence of hamiltonian paths for history based pivot rules on acyclic unique sink orientations of hypercubes"

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.DS(Data Structures and Algorithms)