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 "on the limitations of the univariate marginal distribution algorithm to deception and where bivariate edas might help"):
  Abstract: We introduce a new benchmark problem called Deceptive Leading Blocks (DLB) to rigorously study the runtime of the Univariate Marginal Distribution Algorithm (UMDA) in the presence of epistasis and deception. We show that simple Evolutionary Algorithms (EAs) outperform the UMDA unless the selective pressure µ/λ is extremely high, where µ and λ are the parent and offspring population sizes, respectively. More precisely, we show that the UMDA with a parent population size of µ = Ω (log n) has an expected runtime of eΩ(µ) on the DLB problem assuming any selective pressure [EQUATION], as opposed to the expected runtime of O (nλ log λ + n3) for the non-elitist (µ, λ) EA with µ/λ ≤ 1/e. These results illustrate inherent limitations of univariate EDAs against deception and epistasis, which are common characteristics of real-world problems. In contrast, empirical evidence reveals the efficiency of the bi-variate MIMIC algorithm on the DLB problem. Our results suggest that one should consider EDAs with more complex probabilistic models when optimising problems with some degree of epistasis and deception.

Includes members:
  Member "working principles of binary differential evolution":
    also connected through other hyperedges:
      (none)
  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-1 (induced by source paper "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions")
  Member "multiplicative up drift":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "runtime analysis of fitness proportionate selection on linear functions")
  Member "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions":
    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 "runtime analysis of the univariate marginal distribution algorithm under low selective pressure and prior noise":
    also connected through other hyperedges:
      (none)
  Member "significance based estimation of distribution algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "working principles of binary differential evolution")
  Member "level based analysis of the univariate marginal distribution algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiplicative up drift")
  Member "level based analysis of the population based incremental learning algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "working principles of binary differential evolution")

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)