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: "better runtime guarantees via stochastic domination"
  Abstract: Apart from few exceptions, the mathematical runtime analysis of evolutionary algorithms is mostly concerned with expected runtimes. In this work, we argue that stochastic domination is a notion that should be used more frequently in this area. Stochastic domination allows to formulate much more informative performance guarantees than the expectation alone, it allows to decouple the algorithm analysis into the true algorithmic part of detecting a domination statement and probability theoretic part of deriving the desired probabilistic guarantees from this statement, and it allows simpler and more natural proofs. #R##N#As particular results, we prove a fitness level theorem which shows that the runtime is dominated by a sum of independent geometric random variables, we prove tail bounds for several classic problems, and we give a short and natural proof for Witt's result that the runtime of any $(\mu,p)$ mutation-based algorithm on any function with unique optimum is subdominated by the runtime of a variant of the (1+1) evolutionary algorithm on the OneMax function.

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"
    - "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"
    - "self adaptation of mutation rates in non elitist populations"

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"
    - "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"
    - "ranking based black box complexity"

Connected through hyperedge HE-3 (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"
    - "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"
    - "automated algorithm selection survey and perspectives"

Connected through hyperedge HE-4 (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"
    - "theory of estimation of distribution algorithms"
    - "significance based estimation of distribution algorithms"
    - "the query complexity of a permutation based variant of mastermind"
    - "drift analysis"
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"

Connected through hyperedge HE-5 (induced by source paper "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"
    - "fast artificial immune systems"
    - "theory of estimation of distribution algorithms"
    - "precise runtime analysis for plateaus"
    - "significance based estimation of distribution algorithms"
    - "drift analysis"
    - "fast genetic algorithms"

Connected through hyperedge HE-6 (induced by source paper "interpolating local and global search by controlling the variance of standard bit mutation"):
  with members:
    - "maximizing drift is not optimal for solving onemax"
    - "optimal parameter choices via precise black box analysis"
    - "fast artificial immune systems"
    - "towards a theory guided benchmarking suite for discrete black box optimization heuristics profiling 1 λ ea variants on onemax and leadingones"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "on the effectiveness of simple success based parameter selection mechanisms for two classical discrete black box optimization benchmark problems"
    - "iohprofiler a benchmarking and profiling tool for iterative optimization heuristics"
    - "fast genetic algorithms"

Connected through hyperedge HE-7 (induced by source paper "self adjusting mutation rates with provably optimal success rules"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "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"
    - "adaptive drift analysis"
    - "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-8 (induced by source paper "multiplicative up drift"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "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"
    - "adaptive drift analysis"
    - "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.NE(Neural and Evolutionary Computing)