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 "permutation strikes back the power of recourse in online metric matching"):
  Abstract: In the classical Online Metric Matching problem, we are given a metric space with $k$ servers. A collection of clients arrive in an online fashion, and upon arrival, a client should irrevocably be matched to an as-yet-unmatched server. The goal is to find an online matching which minimizes the total cost, i.e., the sum of distances between each client and the server it is matched to. We know deterministic algorithms~\cite{KP93,khuller1994line} that achieve a competitive ratio of $2k-1$, and this bound is tight for deterministic algorithms. The problem has also long been considered in specialized metrics such as the line metric or metrics of bounded doubling dimension, with the current best result on a line metric being a deterministic $O(\log k)$ competitive algorithm~\cite{raghvendra2018optimal}. Obtaining (or refuting) $O(\log k)$-competitive algorithms in general metrics and constant-competitive algorithms on the line metric have been long-standing open questions in this area. #R##N#In this paper, we investigate the robustness of these lower bounds by considering the Online Metric Matching with Recourse problem where we are allowed to change a small number of previous assignments upon arrival of a new client. Indeed, we show that a small logarithmic amount of recourse can significantly improve the quality of matchings we can maintain. For general metrics, we show a simple \emph{deterministic} $O(\log k)$-competitive algorithm with $O(\log k)$-amortized recourse, an exponential improvement over the $2k-1$ lower bound when no recourse is allowed. We next consider the line metric, and present a deterministic algorithm which is $3$-competitive and has $O(\log k)$-recourse, again a substantial improvement over the best known $O(\log k)$-competitive algorithm when no recourse is allowed.

Includes members:
  Member "stochastic online metric matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "optimal analysis of an online algorithm for the bipartite matching problem on a line":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online metric algorithms with untrusted predictions")
  Member "maintaining perfect matchings at low cost":
    also connected through other hyperedges:
      (none)
  Member "fully dynamic bin packing with limited repacking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "streaming algorithms for bin packing and vector scheduling")
  Member "a match in time saves nine deterministic online matching with delays":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on bin packing with clustering and bin packing with delays")
  Member "a collection of lower bounds for online matching on the line":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online minimum cost matching on the line with recourse")
  Member "online and dynamic algorithms for set cover":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the power of recourse better algorithms for facility location in online and dynamic models")
  Member "online matching haste makes waste":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "putting ridesharing to the test efficient and scalable solutions and the power of dynamic vehicle relocation")

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)