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 "two simple full text indexes based on the suffix array"):
  Abstract: We propose two suffix array inspired full-text indexes. One, called SA-hash, augments the suffix array with a hash table to speed up pattern searches due to significantly narrowed search interval before the binary search phase. The other, called FBCSA, is a compact data structure, similar to M{\"a}kinen's compact suffix array, but working on fixed sized blocks. Experiments on the Pizza~\&~Chili 200\,MB datasets show that SA-hash is about 2--3 times faster in pattern searches (counts) than the standard suffix array, for the price of requiring $0.2n-1.1n$ bytes of extra space, where $n$ is the text length, and setting a minimum pattern length. FBCSA is relatively fast in single cell accesses (a few times faster than related indexes at about the same or better compression), but not competitive if many consecutive cells are to be extracted. Still, for the task of extracting, e.g., 10 successive cells its time-space relation remains attractive.

Includes members:
  Member "alphabet dependent string searching with wexponential search trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "achieving optimal backlog in the vanilla multi processor cup game")
  Member "large scale pattern search using reduced space on disk suffix arrays":
    also connected through other hyperedges:
      (none)
  Member "fixed block compression boosting in fm indexes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed multiple pattern matching")
  Member "compressed text indexes from theory to practice":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a compact representation for trips over networks built on self indexes")

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)