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 "how good is multi pivot quicksort"):
  Abstract: Multi-Pivot Quicksort refers to variants of classical quicksort where in the partitioning step $k$ pivots are used to split the input into $k + 1$ segments. For many years, multi-pivot quicksort was regarded as impractical, but in 2009 a 2-pivot approach by Yaroslavskiy, Bentley, and Bloch was chosen as the standard sorting algorithm in Sun's Java 7. In 2014 at ALENEX, Kushagra et al. introduced an even faster algorithm that uses three pivots. This paper studies what possible advantages multi-pivot quicksort might offer in general. The contributions are as follows: Natural comparison-optimal algorithms for multi-pivot quicksort are devised and analyzed. The analysis shows that the benefits of using multiple pivots with respect to the average comparison count are marginal and these strategies are inferior to simpler strategies such as the well known median-of-$k$ approach. A substantial part of the partitioning cost is caused by rearranging elements. A rigorous analysis of an algorithm for rearranging elements in the partitioning step is carried out, observing mainly how often array cells are accessed during partitioning. The algorithm behaves best if 3 to 5 pivots are used. Experiments show that this translates into good cache behavior and is closest to predicting observed running times of multi-pivot quicksort algorithms. Finally, it is studied how choosing pivots from a sample affects sorting cost. The study is theoretical in the sense that although the findings motivate design recommendations for multipivot quicksort algorithms that lead to running time improvements over known algorithms in an experimental setting, these improvements are small.

Includes members:
  Member "a note on multipivot quicksort":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using non linear difference equations to study quicksort algorithms")
  Member "blockquicksort how branch mispredictions don t affect quicksort":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "even faster sorting of not only integers")
  Member "analysis of pivot sampling in dual pivot quicksort a holistic analysis of yaroslavskiy s partitioning scheme":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (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-5 (induced by source paper "probabilistic analysis of the dual pivot quicksort count")
  Member "optimal partitioning for dual pivot quicksort":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "probabilistic analysis of the dual pivot quicksort count")
  Member "gpu sample sort":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nn sort neural network based data distribution aware sorting")

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)