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: "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"
  Abstract: A few experimental investigations have shown that evolutionary algorithms (EAs) are efficient for the minimum label spanning tree (MLST) problem. However, we know little about that in theory. In this paper, we theoretically analyze the performances of the (1+1) EA, a simple version of EA, and a simple multiobjective evolutionary algorithm called GSEMO on the MLST problem. We reveal that for the    $\mathrm{MLST}_{b}$    problem, the (1+1) EA and GSEMO achieve a    $({b+1})/{2}$   -approximation ratio in expected polynomial runtime with respect to    $n$   , the number of nodes, and    $k$   , the number of labels. We also find that GSEMO achieves a    $(2\ln n+1)$   -approximation ratio for the MLST problem in expected polynomial runtime with respect to    $n$    and    $k$   . At the same time, we show that the (1+1) EA and GSEMO outperform local search algorithms on three instances of the MLST problem. We also construct an instance on which GSEMO outperforms the (1+1) EA.

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"
    - "an analytic expression of relative approximation error for a class of evolutionary algorithms"
    - "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"
    - "an analytic expression of relative approximation error for a class of evolutionary algorithms"
    - "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 "approximating optimization problems using eas on scale free networks"):
  with members:
    - "one size fits all effectiveness of local search on structured data"
    - "greed is good for deterministic scale free networks"
    - "algorithmic complexity of power law networks"
    - "multiplicative drift analysis"

Connected through hyperedge HE-4 (induced by source paper "an analytic expression of relative approximation error for a class of evolutionary algorithms"):
  with members:
    - "average convergence rate of evolutionary algorithms"
    - "on the easiest and hardest fitness functions"
    - "on the approximation ability of evolutionary optimization with application to minimum set cover"

Connected through hyperedge HE-5 (induced by source paper "the sampling and learning framework a statistical view of evolutionary algorithms"):
  with members:
    - "multiplicative drift analysis"
    - "more effective crossover operators for the all pairs shortest path problem"
    - "parameterized runtime analyses of evolutionary algorithms for the euclidean traveling salesperson problem"
    - "on the approximation ability of evolutionary optimization with application to minimum set cover"
    - "on the complexity of trial and error"
    - "a new method for lower bounds on the running time of evolutionary algorithms"
    - "multi view active learning in the non realizable case"

Connected through hyperedge HE-6 (induced by source paper "a theoretical assessment of solution quality in evolutionary algorithms for the knapsack problem"):
  with members:
    - "a novel genetic algorithm using helper objectives for the 0 1 knapsack problem"
    - "mixed strategy may outperform pure strategy an initial study"

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)