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 weighted matching beating the frac 1 2 barrier"):
  Abstract: Online matching and its variants are some of the most fundamental problems in the online algorithms literature. In this paper, we study the online weighted bipartite matching problem. Karp et al. (STOC 1990) gave an elegant algorithm in the unweighted case that achieves a tight competitive ratio of $1-1/e$. In the weighted case, however, we can easily show that no competitive ratio is obtainable without the commonly accepted free disposal assumption. Under this assumption, it is not hard to prove that the greedy algorithm is $1/2$ competitive, and that this is tight for deterministic algorithms. We present the first randomized algorithm that breaks this long-standing $1/2$ barrier and achieves a competitive ratio of at least $0.501$. In light of the hardness result of Kapralov et al. (SODA 2013) that restricts beating a $1/2$ competitive ratio for the monotone submodular welfare maximization problem, our result can be seen as strong evidence that solving the weighted bipartite matching problem is strictly easier than submodular welfare maximization in the online setting. Our approach relies on a very controlled use of randomness, which allows our algorithm to safely make adaptive decisions based on its previous assignments.

Includes members:
  Member "near optimal online algorithms and fast approximation algorithms for resource allocation problems":
    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 "online vertex weighted bipartite matching beating 1 1 e with random arrivals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "online submodular welfare maximization greedy beats 1 2 in random order":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints")
  Member "online submodular welfare maximization greedy is optimal":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints")
  Member "online stochastic packing applied to display ad allocation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "near optimal online algorithms and fast approximation algorithms for resource allocation problems")
  Member "online stochastic matching beating 1 1 e":
    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)