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: "level based analysis of genetic algorithms and other search processes"
  Abstract: Understanding how the time-complexity of evolutionary algorithms (EAs) depend on their parameter settings and characteristics of fitness landscapes is a fundamental problem in evolutionary computation. Most rigorous results were derived using a handful of key analytic techniques, including drift analysis. However, since few of these techniques apply effortlessly to population-based EAs, most time-complexity results concern simplified EAs, such as the (1+1) EA. #R##N#This paper describes the level-based theorem, a new technique tailored to population-based processes. It applies to any non-elitist process where offspring are sampled independently from a distribution depending only on the current population. Given conditions on this distribution, our technique provides upper bounds on the expected time until the process reaches a target state. #R##N#We demonstrate the technique on several pseudo-Boolean functions, the sorting problem, and approximation of optimal solutions in combinatorial optimisation. The conditions of the theorem are often straightforward to verify, even for Genetic Algorithms and Estimation of Distribution Algorithms which were considered highly non-trivial to analyse. Finally, we prove that the theorem is nearly optimal for the processes considered. Given the information the theorem requires about the process, a much tighter bound cannot be proved.

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 "computational complexity analysis of genetic programming"):
  with members:
    - "evolving boolean functions with conjunctions and disjunctions via genetic programming"
    - "on inversely proportional hypermutations with mutation potential"
    - "where are we now a large benchmark study of recent symbolic regression methods"
    - "bounding bloat in genetic programming"
    - "destructiveness of lexicographic parsimony pressure and alleviation by a concatenation crossover in genetic programming"
    - "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"
    - "computational complexity analysis of multi objective genetic programming"
    - "computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics"

Connected through hyperedge HE-3 (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-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"
    - "tight lower bound on the probability of a binomial exceeding its expectation"
    - "adaptive drift analysis"
    - "multiplicative drift analysis"

Connected through hyperedge HE-5 (induced by source paper "runtime analysis of the univariate marginal distribution algorithm under low selective pressure and prior noise"):
  with members:
    - "theory of estimation of distribution algorithms"
    - "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"
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"
    - "theoretical analysis of stochastic search algorithms"
    - "update strength in edas and aco how to avoid genetic drift"

Connected through hyperedge HE-6 (induced by source paper "runtime analysis of fitness proportionate selection on linear functions"):
  with members:
    - "multiplicative up drift"
    - "fast genetic algorithms"
    - "hitting times of local and global optima in genetic algorithms with very high selection pressure"
    - "self adaptation of mutation rates in non elitist populations"
    - "populations can be essential in tracking dynamic optima"

Connected through hyperedge HE-7 (induced by source paper "the efficiency threshold for the offspring population size of the mu lambda ea"):
  with members:
    - "better runtime guarantees via stochastic domination"
    - "drift analysis"
    - "tight lower bound on the probability of a binomial exceeding its expectation"

Connected through hyperedge HE-8 (induced by source paper "on the benefits of populations on the exploitation speed of standard steady state genetic algorithms"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"

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)