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 "stochastic online metric matching"):
  Abstract: We study the minimum-cost metric perfect matching problem under online i.i.d arrivals. We are given a fixed metric with a server at each of the points, and then requests arrive online, each drawn independently from a known probability distribution over the points. Each request has to be matched to a free server, with cost equal to the distance. The goal is to minimize the expected total cost of the matching. #R##N#Such stochastic arrival models have been widely studied for the maximization variants of the online matching problem; however, the only known result for the minimization problem is a tight $O(\log n)$-competitiveness for the random-order arrival model. This is in contrast with the adversarial model, where an optimal competitive ratio of $O(\log n)$ has long been conjectured and remains a tantalizing open question. #R##N#In this paper, we show improved results in the i.i.d arrival model. We show how the i.i.d model can be used to give substantially better algorithms: our main result is an $O((\log \log \log n)^2)$-competitive algorithm in this model. Along the way we give a $9$-competitive algorithm for the line and tree metrics. Both results imply a strict separation between the i.i.d model and the adversarial and random order models, both for general metrics and these much-studied metrics.

Includes members:
  Member "online matching with general arrivals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "tight competitive ratios of classic matching algorithms in the fully online model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models")
  Member "optimal analysis of an online algorithm for the bipartite matching problem on a line":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online metric algorithms with untrusted predictions")
  Member "how to match when all vertices arrive online":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models")
  Member "online vertex weighted bipartite matching beating 1 1 e with random arrivals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "stochastic k server how should uber work":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "putting ridesharing to the test efficient and scalable solutions and the power of dynamic vehicle relocation")
  Member "online allocation with traffic spikes mixing adversarial and stochastic models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "new algorithms better bounds and a novel model for online stochastic matching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online graph matching problems with a worst case reassignment budget")

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)