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 "histogram sort with sampling"):
  Abstract: To minimize data movement, state-of-the-art parallel sorting algorithms use sampling and histogramming techniques to partition keys prior to redistribution. Samples enable partitioning to be done using representative subset of the keys, while histogramming enables evaluation and iterative improvement of a given partitioning. We introduce Histogram sort with sampling (HSS), which combines sampling and histogramming techniques to find high-quality partitions with minimal data movement and high practical performance. Compared to the best known algorithm for finding this partitioning, our algorithm requires a factor of {\Theta}(log(p)/ log log(p)) less communication than the best known (recently introduced) alternative, and substantially less when compared to standard variants of Sample sort and Histogram sort. We provide a distributed-memory implementation of the proposed algorithm and compare its performance to two existing implementations, and provide a brief application study showing the benefit of the new algorithm.

Includes members:
  Member "sorting with asymmetric read and write costs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi asymmetric parallel graph algorithms for nvrams")
  Member "practical massively parallel sorting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communication efficient string 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.DC(Distributed, Parallel, and Cluster Computing)