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 "towards a better understanding of randomized greedy matching"):
  Abstract: There has been a long history for studying randomized greedy matching algorithms since the work by Dyer and Frieze~(RSA 1991). We follow this trend and consider the problem formulated in the oblivious setting, in which the algorithm makes (random) decisions that are essentially oblivious to the input graph.  We revisit the \textsf{Modified Randomized Greedy (MRG)} algorithm by Aronson et al.~(RSA 1995) that is proved to be $(0.5+\epsilon)$-approximate. In particular, we study a weaker version of the algorithm named \textsf{Random Decision Order (RDO)} that in each step, randomly picks an unmatched vertex and matches it to an arbitrary neighbor if exists. We prove the \textsf{RDO} algorithm is $0.639$-approximate and $0.531$-approximate for bipartite graphs and general graphs respectively. As a corollary, we substantially improve the approximation ratio of \textsf{MRG}.  Furthermore, we generalize the \textsf{RDO} algorithm to the edge-weighted case and prove that it achieves a $0.501$ approximation ratio. This result solves the open question by Chan et al.~(SICOMP 2018) about the existence of an algorithm that beats greedy in this setting. As a corollary, it also solves the open questions by Gamlath et al.~(SODA 2019) in the stochastic setting.

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 "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 "how to match when all vertices arrive online":
    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 "matching with our eyes closed":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "opting into optimal matchings")

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)