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: "erratum simplified drift analysis for proving lower bounds in evolutionary computation"
  Abstract: This erratum points out an error in the simplified drift theorem (SDT) [Algorithmica 59(3), 369-386, 2011]. It is also shown that a minor modification of one of its conditions is sufficient to establish a valid result. In many respects, the new theorem is more general than before. We no longer assume a Markov process nor a finite search space. Furthermore, the proof of the theorem is more compact than the previous ones. Finally, previous applications of the SDT are revisited. It turns out that all of these either meet the modified condition directly or by means of few additional arguments.

Connected through hyperedge HE-1 (induced by source paper "significance based estimation of distribution algorithms"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "precise runtime analysis for plateaus"
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"
    - "update strength in edas and aco how to avoid genetic drift"
    - "ranking based black box complexity"

Connected through hyperedge HE-2 (induced by source paper "analysing the robustness of evolutionary algorithms to noise refined runtime bounds and an example where noise is beneficial"):
  with members:
    - "analysis of noisy evolutionary optimization when sampling fails"
    - "memetic algorithms beat evolutionary algorithms on the class of hurdle problems"
    - "analyzing evolutionary optimization in noisy environments"
    - "general drift analysis with tail bounds"

Connected through hyperedge HE-3 (induced by source paper "analysis of noisy evolutionary optimization when sampling fails"):
  with members:
    - "running time analysis of the 1 1 ea for onemax and leadingones under bit wise noise"
    - "analyzing evolutionary optimization in noisy environments"
    - "multiplicative drift analysis"

Connected through hyperedge HE-4 (induced by source paper "runtime analysis of probabilistic crowding and restricted tournament selection for bimodal optimisation"):
  with members:
    - "the benefits of population diversity in evolutionary algorithms a survey of rigorous runtime analyses"
    - "probabilistic tools for the analysis of randomized optimization heuristics"

Connected through hyperedge HE-5 (induced by source paper "theoretical analysis of stochastic search algorithms"):
  with members:
    - "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"
    - "level based analysis of genetic algorithms and other search processes"
    - "reducing the arity in unbiased black box complexity"

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)