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: "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.

Connected through hyperedge HE-1 (induced by source paper "relative worst order analysis a survey"):
  with members:
    - "stochastic dominance and the bijective ratio of online algorithms"
    - "online bounded analysis"
    - "probabilistic analysis of the dual next fit algorithm for bin covering"
    - "the frequent items problem in online streaming under various performance measures"
    - "access graphs results for lru versus fifo under relative worst order analysis"
    - "a comparison of performance measures via online search"
    - "list factoring and relative worst order analysis"
    - "a comparison of performance measures for online algorithms"

Connected through hyperedge HE-2 (induced by source paper "online bin covering expectations vs guarantees"):
  with members:
    - "access graphs results for lru versus fifo under relative worst order analysis"
    - "a comparison of performance measures via online search"
    - "list factoring and relative worst order analysis"
    - "a comparison of performance measures for online algorithms"

Connected through hyperedge HE-3 (induced by source paper "the frequent items problem in online streaming under various performance measures"):
  with members:
    - "access graphs results for lru versus fifo under relative worst order analysis"
    - "a comparison of performance measures via online search"
    - "a comparison of performance measures for online algorithms"

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)