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: "on the approximation ability of evolutionary optimization with application to minimum set cover"
  Abstract: Evolutionary algorithms (EAs) are heuristic algorithms inspired by natural evolution. They are often used to obtain satisficing solutions in practice. In this paper, we investigate a largely underexplored issue: the approximation performance of EAs in terms of how close the solution obtained is to an optimal solution. We study an EA framework named simple EA with isolated population (SEIP) that can be implemented as a single- or multi-objective EA. We analyze the approximation performance of SEIP using the partial ratio, which characterizes the approximation ratio that can be guaranteed. Specifically, we analyze SEIP using a set cover problem that is NP-hard. We find that in a simple configuration, SEIP efficiently achieves an H"n-approximation ratio, the asymptotic lower bound, for the unbounded set cover problem. We also find that SEIP efficiently achieves an (H"k-k-18k^9)-approximation ratio, the currently best-achievable result, for the k-set cover problem. Moreover, for an instance class of the k-set cover problem, we disclose how SEIP, using either one-bit or bit-wise mutation, can overcome the difficulty that limits the greedy algorithm.

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"
    - "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"
    - "multiplicative drift analysis"

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"
    - "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"
    - "multiplicative drift analysis"

Connected through hyperedge HE-3 (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"
    - "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"

Connected through hyperedge HE-4 (induced by source paper "the sampling and learning framework a statistical view of evolutionary algorithms"):
  with members:
    - "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"
    - "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 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-5 (induced by source paper "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"):
  with members:
    - "a large population size can be unhelpful in evolutionary algorithms"
    - "parameterized runtime analyses of evolutionary algorithms for the euclidean traveling salesperson problem"

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)