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 "offspring population size matters when comparing evolutionary algorithms with self adjusting mutation rates"):
  Abstract: We analyze the performance of the 2-rate (1 + λ) Evolutionary Algorithm (EA) with self-adjusting mutation rate control, its 3-rate counterpart, and a (1 + λ) EA variant using multiplicative update rules on the OneMax problem. We compare their efficiency for offspring population sizes ranging up to λ = 3, 200 and problem sizes up to n = 100,000.   Our empirical results show that the ranking of the algorithms is very consistent across all tested dimensions, but strongly depends on the population size. While for small values of λ the 2-rate EA performs best, the multiplicative updates become superior for starting for some threshold value of λ between 50 and 100. Interestingly, for population sizes around 50, the (1 + λ) EA with static mutation rates performs on par with the best of the self-adjusting algorithms.   We also consider how the lower bound pmin for the mutation rate influences the efficiency of the algorithms. We observe that for the 2-rate EA and the EA with multiplicative update rules the more generous bound pmin = 1/n2 gives better results than pmin = 1/n when λ is small. For both algorithms the situation reverses for large λ.

Includes members:
  Member "self adjusting mutation rates with provably optimal success rules":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "simple hyper heuristics control the neighbourhood size of randomised local search optimally for leadingones")
  Member "optimal parameter choices via precise black box analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "complexity theory for discrete black box optimization heuristics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmarking discrete optimization heuristics with iohprofiler")
  Member "towards a theory guided benchmarking suite for discrete black box optimization heuristics profiling 1 λ ea variants on onemax and leadingones":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "benchmarking discrete optimization heuristics with iohprofiler")
  Member "a general dichotomy of evolutionary algorithms on monotone functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interpolating local and global search by controlling the variance of standard bit mutation")
  Member "on the effectiveness of simple success based parameter selection mechanisms for two classical discrete black box optimization benchmark problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interpolating local and global search by controlling the variance of standard bit mutation")
  Member "towards a more practice aware runtime analysis of evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "frequency fitness assignment making optimization algorithms invariant under bijective transformations of the objective function")
  Member "adaptive population models for offspring populations and parallel evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "self adjusting mutation rates with provably optimal success rules")

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)