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: "adaptive drift analysis"
  Abstract: We show that, for any c>0, the (1+1) evolutionary algorithm using an arbitrary mutation rate pn=c/n finds the optimum of a linear objective function over bit strings of length n in expected time ź(nlogn). Previously, this was only known for c≤1. Since previous work also shows that universal drift functions cannot exist for c larger than a certain constant, we instead define drift functions which depend crucially on the relevant objective functions (and also on c itself). Using these carefully-constructed drift functions, we prove that the expected optimisation time is ź(nlogn). By giving an alternative proof of the multiplicative drift theorem, we also show that our optimisation-time bound holds with high probability.

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 "self adjusting mutation rates with provably optimal success rules"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "better runtime guarantees via stochastic domination"
    - "on the effectiveness of simple success based parameter selection mechanisms for two classical discrete black box optimization benchmark problems"
    - "hyper heuristics can achieve optimal performance for pseudo boolean optimisation"
    - "runtime analysis for self adaptive mutation rates"
    - "multiplicative drift analysis"
    - "adaptive population models for offspring populations and parallel evolutionary algorithms"
    - "a new method for lower bounds on the running time of evolutionary algorithms"

Connected through hyperedge HE-4 (induced by source paper "multiplicative up drift"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "better runtime guarantees via stochastic domination"
    - "level based analysis of the univariate marginal distribution algorithm"
    - "level based analysis of the population based incremental learning algorithm"
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"
    - "level based analysis of genetic algorithms and other search processes"
    - "tight lower bound on the probability of a binomial exceeding its expectation"
    - "multiplicative drift analysis"

Connected through hyperedge HE-5 (induced by source paper "evolving boolean functions with conjunctions and disjunctions via genetic programming"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "computational complexity analysis of genetic programming"
    - "bounding bloat in genetic programming"
    - "theoretical analysis of stochastic search algorithms"
    - "multiplicative drift analysis"
    - "computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics"

Connected through hyperedge HE-6 (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"
    - "drift theory in continuous search spaces expected hitting time of the 1 1 es with 1 5 success rule"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "better runtime guarantees via stochastic domination"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "optimal parameter choices via precise black box analysis"
    - "bounding bloat in genetic programming"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "runtime analysis for self adaptive mutation rates"
    - "fast genetic algorithms"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "a tight runtime analysis of the 1 lambda lambda genetic algorithm on onemax"

Connected through hyperedge HE-8 (induced by source paper "a tight runtime analysis for the mu lambda ea"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "better runtime guarantees via stochastic domination"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "runtime analysis for self adaptive mutation rates"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "a lower bound analysis of population based evolutionary algorithms for pseudo boolean functions"
    - "tight lower bound on the probability of a binomial exceeding its expectation"
    - "multiplicative 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.DS(Data Structures and Algorithms)