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 "optimal partitioning for dual pivot quicksort"):
  Abstract: Dual-pivot quicksort refers to variants of classical quicksort where in the partitioning step two pivots are used to split the input into three segments. This can be done in different ways, giving rise to different algorithms. Recently, a dual-pivot algorithm proposed by Yaroslavskiy received much attention, because a variant of it replaced the well-engineered quicksort algorithm in Sun's Java 7 runtime library. Nebel and Wild (ESA 2012) analyzed this algorithm and showed that on average it uses 1.9n ln n + O(n) comparisons to sort an input of size n, beating standard quicksort, which uses 2n ln n + O(n) comparisons. We introduce a model that captures all dual-pivot algorithms, give a unified analysis, and identify new dual-pivot algorithms that minimize the average number of key comparisons among all possible algorithms up to a linear term. This minimum is 1.8n ln n + O(n). For the case that the pivots are chosen from a small sample, we include a comparison of dual-pivot quicksort and classical quicksort. Specifically, we show that dual-pivot quicksort benefits from a skewed choice of pivots. We experimentally evaluate our algorithms and compare them to Yaroslavskiy's algorithm and the recently described three-pivot quicksort algorithm of Kushagra et al. (ALENEX 2014).

Includes members:
  Member "analysis of pivot sampling in dual pivot quicksort a holistic analysis of yaroslavskiy s partitioning scheme":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic analysis of the dual pivot quicksort count")
  Member "analysis of branch misses in quicksort":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysis of pivot sampling in dual pivot quicksort a holistic analysis of yaroslavskiy s partitioning scheme")
  Member "average case and distributional analysis of dual pivot quicksort":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic analysis of the dual pivot quicksort count")
  Member "average case analysis of java 7 s dual pivot quicksort":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic analysis of the dual pivot quicksort count")

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.DS(Data Structures and Algorithms)