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 "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost"):
  Abstract: We prove that the compact genetic algorithm (cGA) with hypothetical population size [MATH HERE] poly(n) with high probability finds the optimum of any n-dimensional jump function with jump size [MATH HERE] ln n in [MATH HERE] iterations. Since it is known that the cGA with high probability needs at least [MATH HERE] iterations to optimize the unimodal OneMax function, our result shows that the cGA in contrast to most classic evolutionary algorithms here is able to cross mo derate-sized valleys of low fitness at no extra cost.   Our runtime guarantee improves over the recent upper bound O(µn1.5 log n) valid for µ = Ω(n3.5+e) of Hasenohrl and Sutton (GECCO 2018). For the best choice of the hypothetical population size, this result gives a runtime guarantee of O(n5+e), whereas ours gives O(n log n).   We also provide a simple general method based on parallel runs that, under mild conditions, (i) overcomes the need to specify a suitable population size, but gives a performance close to the one stemming from the best-possible population size, and (ii) transforms EDAs with high-probability performance guarantees into EDAs with similar bounds on the expected runtime.

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 "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions":
    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 "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions")
  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 "precise runtime analysis for plateaus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "frequency fitness assignment making optimization algorithms invariant under bijective transformations of the objective function")
  Member "significance based estimation of distribution algorithms":
    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 "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions")

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)