human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "analysis of pivot sampling in dual pivot quicksort a holistic analysis of yaroslavskiy s partitioning scheme"
  Abstract: The new dual-pivot Quicksort by Vladimir Yaroslavskiy--used in Oracle's Java runtime library since version 7--features intriguing asymmetries. They make a basic variant of this algorithm use less comparisons than classic single-pivot Quicksort. In this paper, we extend the analysis to the case where the two pivots are chosen as fixed order statistics of a random sample. Surprisingly, dual-pivot Quicksort then needs more comparisons than a corresponding version of classic Quicksort, so it is clear that counting comparisons is not sufficient to explain the running time advantages observed for Yaroslavskiy's algorithm in practice. Consequently, we take a more holistic approach and give also the precise leading term of the average number of swaps, the number of executed Java Bytecode instructions and the number of scanned elements, a new simple cost measure that approximates I/O costs in the memory hierarchy. We determine optimal order statistics for each of the cost measures. It turns out that the asymmetries in Yaroslavskiy's algorithm render pivots with a systematic skew more efficient than the symmetric choice. Moreover, we finally have a convincing explanation for the success of Yaroslavskiy's algorithm in practice: compared with corresponding versions of classic single-pivot Quicksort, dual-pivot Quicksort needs significantly less I/Os, both with and without pivot sampling.

Connected through hyperedge HE-1 (induced by source paper "probabilistic analysis of the dual pivot quicksort count"):
  with members:
    - "how good is multi pivot quicksort"
    - "average case and distributional analysis of dual pivot quicksort"
    - "optimal partitioning for dual pivot quicksort"
    - "average case analysis of java 7 s dual pivot quicksort"

Connected through hyperedge HE-2 (induced by source paper "bidirectional conditional insertion sort algorithm an efficient progress on the classical insertion sort"):
  with members:
    - "average case and distributional analysis of dual pivot quicksort"
    - "optimal partitioning for dual pivot quicksort"
    - "average case analysis of java 7 s dual pivot quicksort"

Connected through hyperedge HE-3 (induced by source paper "how good is multi pivot quicksort"):
  with members:
    - "a note on multipivot quicksort"
    - "blockquicksort how branch mispredictions don t affect quicksort"
    - "analysis of branch misses in quicksort"
    - "average case and distributional analysis of dual pivot quicksort"
    - "optimal partitioning for dual pivot quicksort"
    - "average case analysis of java 7 s dual pivot quicksort"
    - "gpu sample sort"

Connected through hyperedge HE-4 (induced by source paper "why is dual pivot quicksort fast"):
  with members:
    - "average case analysis of java 7 s dual pivot quicksort"
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-5 (induced by source paper "optimal partitioning for dual pivot quicksort"):
  with members:
    - "analysis of branch misses in quicksort"
    - "average case and distributional analysis of dual pivot quicksort"
    - "average case analysis of java 7 s dual pivot quicksort"

Question: Please tell me which class the center node 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)