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: "a new method for lower bounds on the running time of evolutionary algorithms"
  Abstract: We present a new method for proving lower bounds on the expected running time of evolutionary algorithms. It is based on fitness-level partitions and an additional condition on transition probabilities between fitness levels. The method is versatile, intuitive, elegant, and very powerful. It yields exact or near-exact lower bounds for LO, OneMax, long k-paths, and all functions with a unique optimum. Most lower bounds are very general: they hold for all evolutionary algorithms that only use bit-flip mutation as variation operator---i.e. for all selection operators and population models. The lower bounds are stated with their dependence on the mutation rate. #R##N#These results have very strong implications. They allow to determine the optimal mutation-based algorithm for LO and OneMax, i.e., which algorithm minimizes the expected number of fitness evaluations. This includes the choice of the optimal mutation rate.

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 "frequency fitness assignment making optimization algorithms invariant under bijective transformations of the objective function"):
  with members:
    - "quality diversity through surprise"
    - "evaluating map elites on constrained optimization problems"
    - "precise runtime analysis for plateaus"
    - "towards a more practice aware runtime analysis of evolutionary algorithms"
    - "quality and diversity optimization a unifying modular framework"
    - "illuminating search spaces by mapping elites"
    - "a tabu search path relinking algorithm to solve the job shop scheduling problem"
    - "novelty search in competitive coevolution"

Connected through hyperedge HE-3 (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-4 (induced by source paper "evolutionary image transition and painting using random walks"):
  with members:
    - "evolutionary diversity optimization using multi objective indicators"
    - "evolutionary image composition using feature covariance matrices"
    - "ponyge2 grammatical evolution in python"
    - "quasi random agents for image transition and animation"
    - "visual art inspired by the collective feeding behavior of sand bubbler crabs"
    - "perceptual losses for real time style transfer and super resolution"
    - "the evolutionary process of image transition in conjunction with box and strip mutation"
    - "artistic style transfer for videos"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (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"
    - "adaptive drift analysis"
    - "multiplicative drift analysis"
    - "adaptive population models for offspring populations and parallel evolutionary algorithms"

Connected through hyperedge HE-7 (induced by source paper "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"
    - "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"
    - "precise runtime analysis for plateaus"
    - "the query complexity of a permutation based variant of mastermind"
    - "drift analysis"
    - "ranking based black box complexity"
    - "multiplicative drift analysis"

Connected through hyperedge HE-8 (induced by source paper "maximizing drift is not optimal for solving onemax"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "runtime analysis for self adaptive mutation rates"
    - "towards a more practice aware runtime analysis of evolutionary algorithms"
    - "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)