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: "multiplicative drift analysis"
  Abstract: We introduce multiplicative drift analysis as a suitable way to analyze the runtime of randomized search heuristics such as evolutionary algorithms. Our multiplicative version of the classical drift theorem allows easier analyses in the often encountered situation that the optimization progress is roughly proportional to the current distance to the optimum.

Connected through hyperedge HE-1 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "better runtime guarantees via stochastic domination"
    - "runtime analysis for self adaptive mutation rates"
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"
    - "fast genetic algorithms"
    - "runtime analysis of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "update strength in edas and aco how to avoid genetic drift"

Connected through hyperedge HE-2 (induced by source paper "working principles of binary differential evolution"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "fast re optimization via structural diversity"
    - "upper bounds on the running time of the univariate marginal distribution algorithm on onemax"
    - "better runtime guarantees via stochastic domination"
    - "significance based estimation of distribution algorithms"
    - "level based analysis of the population based incremental learning algorithm"
    - "the query complexity of a permutation based variant of mastermind"
    - "update strength in edas and aco how to avoid genetic drift"

Connected through hyperedge HE-3 (induced by source paper "error analysis of elitist random search heuristics"):
  with members:
    - "unlimited budget analysis of randomised search heuristics"
    - "a theoretical framework of approximation error analysis of evolutionary algorithms"
    - "average convergence rate of evolutionary algorithms ii continuous optimization"
    - "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"
    - "on the approximation ability of evolutionary optimization with application to minimum set cover"

Connected through hyperedge HE-4 (induced by source paper "runtime performances of randomized search heuristics for the dynamic weighted vertex cover problem"):
  with members:
    - "parameterized analysis of multi objective evolutionary algorithms and the weighted vertex cover problem"
    - "on the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "chernoff hoeffding inequality and applications"

Connected through hyperedge HE-5 (induced by source paper "parameterized complexity analysis of randomized search heuristics"):
  with members:
    - "parameterized analysis of multi objective evolutionary algorithms and the weighted vertex cover problem"
    - "parameterized runtime analyses of evolutionary algorithms for the euclidean traveling salesperson problem"
    - "evolutionary algorithms and dynamic programming"

Connected through hyperedge HE-6 (induced by source paper "benchmarking discrete optimization heuristics with iohprofiler"):
  with members:
    - "offspring population size matters when comparing evolutionary algorithms with self adjusting mutation rates"
    - "interpolating local and global search by controlling the variance of standard bit mutation"
    - "hyper parameter tuning for the 1 λ λ ga"
    - "optimal parameter choices via precise black box analysis"
    - "better runtime guarantees via stochastic domination"
    - "theory of estimation of distribution algorithms"
    - "complexity theory for discrete black box optimization heuristics"
    - "towards a theory guided benchmarking suite for discrete black box optimization heuristics profiling 1 λ ea variants on onemax and leadingones"

Connected through hyperedge HE-7 (induced by source paper "on the limitations of the univariate marginal distribution algorithm to deception and where bivariate edas might help"):
  with members:
    - "working principles of binary differential evolution"
    - "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost"
    - "multiplicative up drift"
    - "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"
    - "runtime analysis of the univariate marginal distribution algorithm under low selective pressure and prior noise"
    - "significance based estimation of distribution algorithms"
    - "level based analysis of the univariate marginal distribution algorithm"
    - "level based analysis of the population based incremental learning algorithm"

Connected through hyperedge HE-8 (induced by source paper "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost"
    - "fast artificial immune systems"
    - "better runtime guarantees via stochastic domination"
    - "theory of estimation of distribution algorithms"
    - "significance based estimation of distribution algorithms"
    - "the query complexity of a permutation based variant of mastermind"
    - "drift analysis"

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)