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 graph matching problems with a worst case reassignment budget"):
  Abstract: In the online bipartite matching with reassignments problem, an algorithm is initially given only one side of the vertex set of a bipartite graph; the vertices on the other side are revealed to the algorithm one by one, along with its incident edges. The algorithm is required to maintain a matching in the current graph, where the algorithm revises the matching after each vertex arrival by reassigning vertices. Bernstein, Holm, and Rotenberg showed that an online algorithm can maintain a matching of maximum cardinality by performing amortized $O(\log^2 n)$ reassignments per arrival. #R##N#In this paper, we propose to consider the general question of how requiring a non-amortized hard budget $k$ on the number of reassignments affects the algorithms' performances, under various models from the literature. #R##N#We show that a simple, widely-used algorithm is a best-possible deterministic algorithm for all these models. For the unweighted maximum-cardinality problem, the algorithm is a $(1-\frac{2}{k+2})$-competitive algorithm, which is the best possible for a deterministic algorithm both under vertex arrivals and edge arrivals. Applied to the load balancing problem, this yields a bifactor online algorithm. For the weighted problem, which is traditionally studied assuming the triangle inequality, we show that the power of reassignment allows us to lift this assumption and the algorithm becomes a $\frac{1}{2}$-competitive algorithm for $k=4$, improving upon the $\frac{1}{3}$ of the previous algorithm without reassignments. We show that this also is a best-possible deterministic algorithm.

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 "independent sets of dynamic rectangles algorithms and experiments")
  Member "stochastic online metric matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the online min sum set cover problem")
  Member "understanding zadimoghaddam s edge weighted online matching algorithm unweighted case":
    also connected through other hyperedges:
      (none)
  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 "online maximum matching with recourse":
    also connected through other hyperedges:
      (none)
  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 vertex weighted bipartite matching beating 1 1 e with random arrivals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online primal dual meets online matching with stochastic rewards configuration lp to the rescue")

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)