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: "an analytic expression of relative approximation error for a class of evolutionary algorithms"
  Abstract: An important question in evolutionary computation is how good solutions evolutionary algorithms can produce. This paper aims to provide an analytic analysis of solution quality in terms of the relative approximation error, which is defined by the error between 1 and the approximation ratio of the solution found by an evolutionary algorithm. Since evolutionary algorithms are iterative methods, the relative approximation error is a function of generations. With the help of matrix analysis, it is possible to obtain an exact expression of such a function. In this paper, an analytic expression for calculating the relative approximation error is presented for a class of evolutionary algorithms, that is, (1+1) strictly elitist evolution algorithms. Furthermore, analytic expressions of the fitness value and the average convergence rate in each generation are also derived for this class of evolutionary algorithms. The approach is promising, and it can be extended to non-elitist or population-based algorithms too.

Connected through hyperedge HE-1 (induced by source paper "error analysis of elitist random search heuristics"):
  with members:
    - "unlimited budget analysis of randomised search heuristics"
    - "a theoretical framework of approximation error analysis of evolutionary algorithms"
    - "average convergence rate of evolutionary algorithms ii continuous optimization"
    - "average convergence rate of evolutionary algorithms"
    - "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"
    - "multiplicative drift analysis"
    - "on the approximation ability of evolutionary optimization with application to minimum set cover"

Connected through hyperedge HE-2 (induced by source paper "estimating approximation errors of elitist evolutionary algorithms"):
  with members:
    - "drift theory in continuous search spaces expected hitting time of the 1 1 es with 1 5 success rule"
    - "a theoretical framework of approximation error analysis of evolutionary algorithms"
    - "average convergence rate of evolutionary algorithms"
    - "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"
    - "multiplicative drift analysis"
    - "on the approximation ability of evolutionary optimization with application to minimum set cover"

Connected through hyperedge HE-3 (induced by source paper "unlimited budget analysis of randomised search heuristics"):
  with members:
    - "a theoretical framework of approximation error analysis of evolutionary algorithms"
    - "optimal parameter choices via precise black box analysis"
    - "average convergence rate 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)