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 "multiplicative up drift"):
  Abstract: Drift analysis aims at translating the expected progress of an evolutionary algorithm (or more generally, a random process) into a probabilistic guarantee on its run time (hitting time). So far, drift arguments have been successfully employed in the rigorous analysis of evolutionary algorithms, however, only for the situation that the progress is constant or becomes weaker when approaching the target.   Motivated by questions like how fast fit individuals take over a population, we analyze random processes exhibiting a multiplicative growth in expectation. We prove a drift theorem translating this expected progress into a hitting time. This drift theorem gives a simple and insightful proof of the level-based theorem first proposed by Lehre (2011). Our version of this theorem has, for the first time, the best-possible linear dependence on the growth parameter Δ (the previous-best was quadratic). This gives immediately stronger run time guarantees for a number of applications.

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 "better runtime guarantees via stochastic domination":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "level based analysis of the univariate marginal distribution algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the limitations of the univariate marginal distribution algorithm to deception and where bivariate edas might help")
  Member "level based analysis of the population based incremental learning algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "working principles of binary differential evolution")
  Member "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "level based analysis of genetic algorithms and other search processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "tight lower bound on the probability of a binomial exceeding its expectation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "adaptive drift analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")

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)