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 "online stochastic matching with edge arrivals"):
  Abstract: Online bipartite matching with edge arrivals is an important extension of the classic vertex-arrival model of Karp, Vazirani and Vazirani. Finding an algorithm better than the straightforward greedy strategy remained a major open question for a long time until a recent negative result by Gamlath et al. (forthcoming FOCS 2019), who showed that no online algorithm has a competitive ratio better than $0.5$ in the worst case. #R##N#In this work, we consider the bipartite matching problem with edge arrivals in the stochastic framework. We find an online algorithm that on average is $0.506$-competitive. We give a supplementary upper bound of $\frac{2}{3}$ and also obtain along the way an interesting auxiliary result that if the initial instance has a $2$-regular stochastic subgraph, then a natural prune & greedy algorithm matches at least $0.532$-fraction of all vertices.

Includes members:
  Member "beating greedy for stochastic bipartite matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models")
  Member "online matching with general arrivals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "prophet inequality for bipartite matching merits of being simple and non adaptive":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models")
  Member "tight competitive ratios of classic matching algorithms in the fully online model":
    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 "how to match when all vertices arrive online":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models")
  Member "online matroid intersection beating half for random arrival":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved truthful mechanisms for combinatorial auctions with submodular bidders")
  Member "improved approximation algorithms for stochastic matching":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the markovian price of information")
  Member "improved bounds for online preemptive matching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "prophet inequality for bipartite matching merits of being simple and non adaptive")

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)