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: "access graphs results for lru versus fifo under relative worst order analysis"
  Abstract: Access graphs, which have been used previously in connection with competitive analysis to model locality of reference in paging, are considered in connection with relative worst order analysis. In this model, FWF is shown to be strictly worse than both LRU and FIFO on any access graph. LRU is shown to be strictly better than FIFO on paths and cycles, but they are incomparable on some families of graphs which grow with the length of the sequences.

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"
    - "relative interval analysis of paging algorithms on access graphs"
    - "the frequent items problem in online streaming under various performance measures"
    - "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 multi coloring with advice"):
  with members:
    - "online bin packing with advice"

Connected through hyperedge HE-3 (induced by source paper "relative interval analysis of paging algorithms on access graphs"):
  with members:
    - "list factoring and relative worst order analysis"
    - "a comparison of performance measures for online algorithms"
    - "on line paging against adversarially biased random inputs"
    - "truly online paging with locality of reference"
    - "the k server dual and loose competitiveness for paging"

Connected through hyperedge HE-4 (induced by source paper "online bin covering expectations vs guarantees"):
  with members:
    - "relative interval analysis of paging algorithms on access graphs"
    - "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-5 (induced by source paper "the frequent items problem in online streaming under various performance measures"):
  with members:
    - "relative interval analysis of paging algorithms on access graphs"
    - "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)