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 "the sampling and learning framework a statistical view of evolutionary algorithms"):
  Abstract: Evolutionary algorithms (EAs), a large class of general purpose optimization algorithms inspired from the natural phenomena, are widely used in various industrial optimizations and often show excellent performance. This paper presents an attempt towards revealing their general power from a statistical view of EAs. By summarizing a large range of EAs into the sampling-and-learning framework, we show that the framework directly admits a general analysis on the probable-absolute-approximate (PAA) query complexity. We particularly focus on the framework with the learning subroutine being restricted as a binary classification, which results in the sampling-and-classification (SAC) algorithms. With the help of the learning theory, we obtain a general upper bound on the PAA query complexity of SAC algorithms. We further compare SAC algorithms with the uniform search in different situations. Under the error-target independence condition, we show that SAC algorithms can achieve polynomial speedup to the uniform search, but not super-polynomial speedup. Under the one-side-error condition, we show that super-polynomial speedup can be achieved. This work only touches the surface of the framework. Its power under other conditions is still open.

Includes members:
  Member "performance analysis of evolutionary algorithms for the minimum label spanning tree problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "error analysis of elitist random search heuristics")
  Member "multiplicative drift analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "more effective crossover operators for the all pairs shortest path problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "black box complexity of the binary value function")
  Member "parameterized runtime analyses of evolutionary algorithms for the euclidean traveling salesperson problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parameterized complexity analysis of randomized search heuristics")
  Member "on the approximation ability of evolutionary optimization with application to minimum set cover":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "error analysis of elitist random search heuristics")
  Member "on the complexity of trial and error":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the dependent doors problem an investigation into sequential decisions without feedback")
  Member "a new method for lower bounds on the running time of evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "multi view active learning in the non realizable case":
    also connected through other hyperedges:
      (none)

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)