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: "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).

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

Connected through hyperedge HE-2 (induced by source paper "analysis of pivot sampling in dual pivot quicksort a holistic analysis of yaroslavskiy s partitioning scheme"):
  with members:
    - "analysis of quickselect under yaroslavskiy s dual pivoting algorithm"
    - "analysis of branch misses in quicksort"
    - "average case and distributional analysis of dual pivot quicksort"
    - "pivot sampling in dual pivot quicksort"
    - "average case analysis of java 7 s dual pivot quicksort"

Connected through hyperedge HE-3 (induced by source paper "bidirectional conditional insertion sort algorithm an efficient progress on the classical insertion sort"):
  with members:
    - "analysis of pivot sampling in dual pivot quicksort a holistic analysis of yaroslavskiy s partitioning scheme"
    - "average case and distributional analysis of dual pivot quicksort"
    - "average case analysis of java 7 s dual pivot quicksort"

Connected through hyperedge HE-4 (induced by source paper "analysis of quickselect under yaroslavskiy s dual pivoting algorithm"):
  with members:
    - "average case and distributional analysis of dual pivot quicksort"
    - "average case analysis of java 7 s dual pivot quicksort"

Connected through hyperedge HE-5 (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 pivot sampling in dual pivot quicksort a holistic analysis of yaroslavskiy s partitioning scheme"
    - "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"
    - "gpu sample sort"

Connected through hyperedge HE-6 (induced by source paper "pivot sampling in dual pivot quicksort"):
  with members:
    - "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)