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 "engineering faster sorters for small sets of elements"):
  Abstract: Sorting a set of items is a task that can be useful by itself or as a building block for more complex operations. The more sophisticated and fast sorting algorithms become asymptotically, the less efficient they are for small sets of items due to large constant factor. This thesis aims to determine if there is a faster way to sort base case sizes than using insertion sort. For that we looked at sorting networks and how to implement them efficiently. Because sorting networks need to be implemented explicitly for each input size, providing networks for larger sizes becomess less efficient. That is why we modified Super Scalar Sample Sort to break down larger sets into sizes that can in turn be sorted by sorting networks. We show that the task of sorting only small sets can be greatly improved by at least 25% when using sorting networks compared to insertion sort, but that when integrating them into other sorting algorithms the speed-up is hindered by the limited L1 instruction cache size. On a machine with 64KiB of L1 instruction cache we achieved over 6% of improvement when using sorting networks as a base case sorter instead of insertion sort.

Includes members:
  Member "scalable string and suffix sorting algorithms techniques and tools":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication efficient string sorting")
  Member "in place parallel super scalar samplesort ips 4 o":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "high quality shared memory graph partitioning")
  Member "robust massively parallel sorting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication efficient string sorting")
  Member "twenty five comparators is optimal when sorting nine inputs and twenty nine for ten":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "design space exploration as quantified satisfaction")
  Member "parallel string sample sort":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication efficient string sorting")
  Member "gpu sample sort":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)