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: "average case and distributional analysis of dual pivot quicksort"
  Abstract: In 2009, Oracle replaced the long-serving sorting algorithm in its Java 7 runtime library by a new dual-pivot Quicksort variant due to Vladimir Yaroslavskiy. The decision was based on the strikingly good performance of Yaroslavskiy's implementation in running time experiments. At that time, no precise investigations of the algorithm were available to explain its superior performance—on the contrary: previous theoretical studies of other dual-pivot Quicksort variants even discouraged the use of two pivots. In 2012, two of the authors gave an average case analysis of a simplified version of Yaroslavskiy's algorithm, proving that savings in the number of comparisons are possible. However, Yaroslavskiy's algorithm needs more swaps, which renders the analysis inconclusive.   To force the issue, we herein extend our analysis to the fully detailed style of Knuth: we determine the exact number of executed Java Bytecode instructions. Surprisingly, Yaroslavskiy's algorithm needs sightly more Bytecode instructions than a simple implementation of classic Quicksort—contradicting observed running times. As in Oracle's library implementation, we incorporate the use of Insertionsort on small subproblems and show that it indeed speeds up Yaroslavskiy's Quicksort in terms of Bytecodes; but even with optimal Insertionsort thresholds, the new Quicksort variant needs slightly more Bytecode instructions on average.   Finally, we show that the (suitably normalized) costs of Yaroslavskiy's algorithm converge to a random variable whose distribution is characterized by a fixed-point equation. From that, we compute variances of costs and show that for large n, costs are concentrated around their mean.

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"
    - "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 "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"
    - "pivot sampling in 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 "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"
    - "optimal partitioning for dual pivot quicksort"
    - "average case analysis of java 7 s dual pivot quicksort"

Connected through hyperedge HE-4 (induced by source paper "blockquicksort how branch mispredictions don t affect quicksort"):
  with members:
    - "how good is multi pivot quicksort"
    - "analysis of branch misses in quicksort"
    - "average case analysis of java 7 s dual pivot quicksort"

Connected through hyperedge HE-5 (induced by source paper "analysis of quickselect under yaroslavskiy s dual pivoting algorithm"):
  with members:
    - "optimal partitioning for dual pivot quicksort"
    - "average case analysis of java 7 s dual pivot quicksort"

Connected through hyperedge HE-6 (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"
    - "optimal partitioning for dual pivot quicksort"
    - "average case analysis of java 7 s dual pivot quicksort"
    - "gpu sample sort"

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

Connected through hyperedge HE-8 (induced by source paper "optimal partitioning for dual pivot quicksort"):
  with members:
    - "analysis of pivot sampling in dual pivot quicksort a holistic analysis of yaroslavskiy s partitioning scheme"
    - "analysis of branch misses in 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)