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 "a competitive ratio approximation scheme for the k server problem in fixed finite metrics"):
  Abstract: We show how to restrict the analysis of a class of online problems that includes the $k$-server problem in finite metrics such that we only have to consider finite sequences of request. When applying the restrictions, both the optimal offline solutions and the best possible deterministic or randomized online solutions only differ by at most an arbitrarily small constant factor from the corresponding solutions without restrictions. Furthermore, we show how to obtain an algorithm with best possible deterministic or randomized competitive ratio for the restricted setup. Thus, for each fixed finite metrics our result qualifies as a competitive ratio approximation scheme as defined by G\"unther et al.

Includes members:
  Member "competitive ratio approximation schemes for minimizing the makespan in the online list model":
    also connected through other hyperedges:
      (none)
  Member "randomized online computation with high probability guarantees":
    also connected through other hyperedges:
      (none)
  Member "a new approach to online scheduling approximating the optimal competitive ratio":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "randomization can be as helpful as a glimpse of the future in online computation")
  Member "a polylogarithmic competitive algorithm for the k server problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic k server how should uber work")
  Member "ramsey type theorems for metric spaces with applications to online problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "competitive ratio versus regret minimization achieving the best of both worlds")
  Member "better algorithms for unfair metrical task systems and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "competitive ratio versus regret minimization achieving the best of both worlds")
  Member "competitive paging algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online metric algorithms with untrusted predictions")

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)