human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "stochastic online metric matching"
  Abstract: We study the minimum-cost metric perfect matching problem under online i.i.d arrivals. We are given a fixed metric with a server at each of the points, and then requests arrive online, each drawn independently from a known probability distribution over the points. Each request has to be matched to a free server, with cost equal to the distance. The goal is to minimize the expected total cost of the matching. #R##N#Such stochastic arrival models have been widely studied for the maximization variants of the online matching problem; however, the only known result for the minimization problem is a tight $O(\log n)$-competitiveness for the random-order arrival model. This is in contrast with the adversarial model, where an optimal competitive ratio of $O(\log n)$ has long been conjectured and remains a tantalizing open question. #R##N#In this paper, we show improved results in the i.i.d arrival model. We show how the i.i.d model can be used to give substantially better algorithms: our main result is an $O((\log \log \log n)^2)$-competitive algorithm in this model. Along the way we give a $9$-competitive algorithm for the line and tree metrics. Both results imply a strict separation between the i.i.d model and the adversarial and random order models, both for general metrics and these much-studied metrics.

Connected through hyperedge HE-1 (induced by source paper "online graph matching problems with a worst case reassignment budget"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "online matching with general arrivals"
    - "understanding zadimoghaddam s edge weighted online matching algorithm unweighted case"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "online maximum matching with recourse"
    - "how to match when all vertices arrive online"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"
    - "relaxing the irrevocability requirement for online graph algorithms"

Connected through hyperedge HE-2 (induced by source paper "the online min sum set cover problem"):
  with members:
    - "chasing convex bodies optimally"
    - "pure entropic regularization for metrical task systems"
    - "metrical task systems on trees via mirror descent and unfair gluing"
    - "weighted k server bounds via combinatorial dichotomies"
    - "competitive algorithms for generalized k server in uniform metrics"
    - "a static optimality transformation with applications to planar point location"
    - "the generalized work function algorithm is competitive for the generalized 2 server problem"
    - "a note on the generalized min sum set cover problem"

Connected through hyperedge HE-3 (induced by source paper "permutation strikes back the power of recourse in online metric matching"):
  with members:
    - "optimal analysis of an online algorithm for the bipartite matching problem on a line"
    - "maintaining perfect matchings at low cost"
    - "fully dynamic bin packing with limited repacking"
    - "a match in time saves nine deterministic online matching with delays"
    - "a collection of lower bounds for online matching on the line"
    - "online and dynamic algorithms for set cover"
    - "online matching haste makes waste"
    - "new algorithms better bounds and a novel model for online stochastic matching"

Question: Please tell me which class the center node 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)