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 bipartite matching with decomposable weights"):
  Abstract: We study a weighted online bipartite matching problem: $G(V_1, V_2, E)$ is a weighted bipartite graph where $V_1$ is known beforehand and the vertices of $V_2$ arrive online. The goal is to match vertices of $V_2$ as they arrive to vertices in $V_1$, so as to maximize the sum of weights of edges in the matching. If assignments to $V_1$ cannot be changed, no bounded competitive ratio is achievable. We study the weighted online matching problem with {\em free disposal}, where vertices in $V_1$ can be assigned multiple times, but only get credit for the maximum weight edge assigned to them over the course of the algorithm. For this problem, the greedy algorithm is $0.5$-competitive and determining whether a better competitive ratio is achievable is a well known open problem. #R##N#We identify an interesting special case where the edge weights are decomposable as the product of two factors, one corresponding to each end point of the edge. This is analogous to the well studied related machines model in the scheduling literature, although the objective functions are different. For this case of decomposable edge weights, we design a 0.5664 competitive randomized algorithm in complete bipartite graphs. We show that such instances with decomposable weights are non-trivial by establishing upper bounds of 0.618 for deterministic and $0.8$ for randomized algorithms. #R##N#A tight competitive ratio of $1-1/e \approx 0.632$ was known previously for both the 0-1 case as well as the case where edge weights depend on the offline vertices only, but for these cases, reassignments cannot change the quality of the solution. Beating 0.5 for weighted matching where reassignments are necessary has been a significant challenge. We thus give the first online algorithm with competitive ratio strictly better than 0.5 for a non-trivial case of weighted matching with free disposal.

Includes members:
  Member "matching with our eyes closed":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards a better understanding of randomized greedy matching")
  Member "improved bounds for online preemptive matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prophet inequality for bipartite matching merits of being simple and non adaptive")
  Member "buyback problem approximate matroid intersection with cancellation costs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online stochastic matching with edge arrivals")
  Member "online vertex weighted bipartite matching and single bid budgeted allocations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "a dynamic near optimal algorithm for online linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "random order models")
  Member "randomized online algorithms for the buyback problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online knapsack problems with a resource buffer")
  Member "online stochastic matching beating 1 1 e":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)