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 "relative interval analysis of paging algorithms on access graphs"):
  Abstract: Access graphs, which have been used previously in connection with competitive analysis and relative worst order analysis to model locality of reference in paging, are considered in connection with relative interval analysis. The algorithms LRU, FIFO, FWF, and FAR are compared using the path, star, and cycle access graphs. In this model, some of the expected results are obtained. However, although LRU is found to be strictly better than FIFO on paths, it has worse performance on stars, cycles, and complete graphs, in this model. We solve an open question from [Dorrigiv, Lopez-Ortiz, Munro, 2009], obtaining tight bounds on the relationship between LRU and FIFO with relative interval analysis.

Includes members:
  Member "access graphs results for lru versus fifo under relative worst order analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relative worst order analysis a survey")
  Member "list factoring and relative worst order analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relative worst order analysis a survey")
  Member "a comparison of performance measures for online algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmark design and prior independent optimization")
  Member "on line paging against adversarially biased random inputs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "practical bounds on optimal caching with variable object sizes")
  Member "truly online paging with locality of reference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic k server how should uber work")
  Member "the k server dual and loose competitiveness for paging":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cache optimization models and algorithms")

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)