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 "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"):
  Abstract: In the first runtime analysis of an estimation-of-distribution algorithm (EDA) on the multimodal jump function class, Hasenohrl and Sutton (GECCO 2018) proved that the runtime of the compact genetic algorithm with suitable parameter choice on jump functions with high probability is at most polynomial (in the dimension) if the jump size is at most logarithmic (in the dimension), and is at most exponential in the jump size if the jump size is super-logarithmic. The exponential runtime guarantee was achieved with a hypothetical population size that is also exponential in the jump size. Consequently, this setting cannot lead to a better runtime.   In this work, we show that any choice of the hypothetical population size leads to a runtime that, with high probability, is at least exponential in the jump size. This result might be the first non-trivial exponential lower bound for EDAs that holds for arbitrary parameter settings.

Includes members:
  Member "probabilistic tools for the analysis of randomized optimization heuristics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "working principles of binary differential evolution")
  Member "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the limitations of the univariate marginal distribution algorithm to deception and where bivariate edas might help")
  Member "fast artificial immune systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost")
  Member "better runtime guarantees via stochastic domination":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "theory of estimation of distribution algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "benchmarking discrete optimization heuristics with iohprofiler")
  Member "significance based estimation of distribution algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "working principles of binary differential evolution")
  Member "the query complexity of a permutation based variant of mastermind":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "working principles of binary differential evolution")
  Member "drift analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost")

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)