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: "drift theory in continuous search spaces expected hitting time of the 1 1 es with 1 5 success rule"
  Abstract: This paper explores the use of the standard approach for proving runtime bounds in discrete domains---often referred to as drift analysis---in the context of optimization on a continuous domain. Using this framework we analyze the (1+1) Evolution Strategy with one-fifth success rule on the sphere function. To deal with potential functions that are not lower-bounded, we formulate novel drift theorems. We then use the theorems to prove bounds on the expected hitting time to reach a certain target fitness in finite dimension $d$. The bounds are akin to linear convergence. We then study the dependency of the different terms on $d$ proving a convergence rate dependency of $\Theta(1/d)$. Our results constitute the first non-asymptotic analysis for the algorithm considered as well as the first explicit application of drift analysis to a randomized search heuristic with continuous domain.

Connected through hyperedge HE-1 (induced by source paper "estimating approximation errors of elitist evolutionary algorithms"):
  with members:
    - "a theoretical framework of approximation error analysis of evolutionary algorithms"
    - "average convergence rate of evolutionary algorithms"
    - "an analytic expression of relative approximation error for a class of evolutionary algorithms"
    - "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"
    - "multiplicative drift analysis"
    - "on the approximation ability of evolutionary optimization with application to minimum set cover"

Connected through hyperedge HE-2 (induced by source paper "runtime analysis for self adaptive mutation rates"):
  with members:
    - "theory of parameter control for discrete black box optimization provable performance gains through dynamic parameter choices"
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "optimal parameter choices via precise black box analysis"
    - "better runtime guarantees via stochastic domination"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "self adaptation of mutation rates in non elitist populations"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "level based analysis of genetic algorithms and other search processes"

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.NE(Neural and Evolutionary Computing)