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 analysis of java 7 s dual pivot quicksort"
  Abstract: Recently, a new Quicksort variant due to Yaroslavskiy was chosen as standard sorting method for Oracle's Java 7 runtime library. The decision for the change was based on empirical studies showing that on average, the new algorithm is faster than the formerly used classic Quicksort. Surprisingly, the improvement was achieved by using a dual pivot approach, an idea that was considered not promising by several theoretical studies in the past. In this paper, we identify the reason for this unexpected success. Moreover, we present the first precise average case analysis of the new algorithm showing e.g. that a random permutation of length n is sorted using $1.9n\ln n-2.46n+\mathcal{O}(\ln n)$ key comparisons and $0.6n\ln n+0.08n+\mathcal{O}(\ln n)$ swaps.

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"
    - "optimal partitioning for 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"
    - "optimal partitioning for 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"
    - "optimal partitioning for 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 and distributional analysis of dual pivot quicksort"

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

Connected through hyperedge HE-7 (induced by source paper "why is dual pivot quicksort fast"):
  with members:
    - "analysis of pivot sampling in dual pivot quicksort a holistic analysis of yaroslavskiy s partitioning scheme"
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-8 (induced by source paper "analysis of branch misses in quicksort"):
  with members:
    - "average case and distributional analysis of dual pivot quicksort"
    - "pivot sampling in 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)