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: "stochastic runtime analysis of a cross entropy algorithm for traveling salesman problems"
  Abstract: Abstract   This article analyzes the stochastic runtime of a Cross-Entropy algorithm mimicking an Max-Min Ant System with iteration-best reinforcement. It investigates the impact of magnitude of the sample size on the runtime to find optimal solutions for TSP instances.  For simple TSP instances that have a    {  1  ,  n  }   -valued distance function and a unique optimal solution, we show that sample size    N  ∈  ω  (  ln  ⁡  n  )    results in a stochastically polynomial runtime, and    N  ∈  O  (  ln  ⁡  n  )    results in a stochastically exponential runtime, where “stochastically” means with a probability of    1  −    n    −  ω  (  1  )     , and n represents number of cities. In particular, for    N  ∈  ω  (  ln  ⁡  n  )   , we prove a stochastic runtime of    O  (  N  ⋅    n    6    )    with the vertex-based random solution generation, and a stochastic runtime of    O  (  N  ⋅    n    3    ln  ⁡  n  )    with the edge-based random solution generation. These runtimes are very close to the best known expected runtime for variants of Max-Min Ant System with best-so-far reinforcement by choosing a small    N  ∈  ω  (  ln  ⁡  n  )   . They are obtained for the stronger notion of stochastic runtime, and analyze the runtime in most cases.  We also inspect more complex instances with n vertices positioned on an    m  ×  m    grid. When the n vertices span a convex polygon, we obtain a stochastic runtime of    O  (    n    4      m    5  +  ϵ    )    with the vertex-based random solution generation, and a stochastic runtime of    O  (    n    3      m    5  +  ϵ    )    for the edge-based random solution generation. When there are    k  ∈  O  (  1  )    many vertices inside a convex polygon spanned by the other    n  −  k    vertices, we obtain a stochastic runtime of    O  (    n    4      m    5  +  ϵ    +    n    6  k  −  1      m    ϵ    )    with the vertex-based random solution generation, and a stochastic runtime of    O  (    n    3      m    5  +  ϵ    +    n    3  k      m    ϵ    )    with the edge-based random solution generation. These runtimes are better than the expected runtime for the so-called    (  μ  +  λ  )    EA reported in a recent article, and again obtained for the stronger notion of stochastic runtime.

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)