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 "communication efficient weighted reservoir sampling from fully distributed data streams"):
  Abstract: We consider communication-efficient weighted and unweighted (uniform) random sampling from distributed data streams presented as a sequence of mini-batches of items. This is a natural model for distributed streaming computation, and our goal is to showcase its usefulness. We present and analyze fully distributed, communication-efficient algorithms for both versions of the problem. An experimental evaluation of weighted reservoir sampling on up to 256 nodes (5120 processors) shows good speedups, while theoretical analysis promises further scaling to much larger machines.

Includes members:
  Member "optimal random sampling from distributed streams revisited":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "weighted reservoir sampling from distributed streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "parallel weighted random sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances in scalable network generation")
  Member "parallel streaming random sampling":
    also connected through other hyperedges:
      (none)
  Member "efficient parallel random sampling vectorized cache efficient and online":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recent advances in scalable network generation")
  Member "weighted sampling without replacement from data streams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the adversarial robustness of sampling")
  Member "fast parallel operations on search trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal parallel algorithms in the binary forking model")

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)