human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "error analysis of elitist random search heuristics"):
  Abstract: When random search heuristics (RSH) cannot locate precise globally optimal solutions with satisfactory performances, hitting time/running time analysis method is not flexible enough to accommodate the requirement of theoretical performance study. Thus, an alternative routine is needed to bridge the gap between theoretical analysis and algorithm implementation. Inspired by this idea, this paper is dedicated to perform an error analysis for elitist RSHs. By diagonalizing the transition matrix of a Markov chain model, the $t$-step transition matrix can be computed for any iteration budget $t$. Then, A general framework for estimation of expected approximation errors can be proposed. Case studies indicate that the error analysis works well for both uni- and multi-modal benchmark problems. It leads to precise estimations of approximation error instead of asymptotic results on fitness values, which demonstrates its competitiveness to fixed budget analysis.

Includes members:
  Member "unlimited budget analysis of randomised search heuristics":
    also connected through other hyperedges:
      (none)
  Member "a theoretical framework of approximation error analysis of evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimating approximation errors of elitist evolutionary algorithms")
  Member "average convergence rate of evolutionary algorithms ii continuous optimization":
    also connected through other hyperedges:
      (none)
  Member "average convergence rate of evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimating approximation errors of elitist evolutionary algorithms")
  Member "an analytic expression of relative approximation error for a class of evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating approximation errors of elitist evolutionary algorithms")
  Member "performance analysis of evolutionary algorithms for the minimum label spanning tree problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "estimating approximation errors of elitist evolutionary algorithms")
  Member "multiplicative drift analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "on the approximation ability of evolutionary optimization with application to minimum set cover":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "estimating approximation errors of elitist evolutionary algorithms")

Question: Please tell me which class the source paper 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)