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 "fm index for dummies"):
  Abstract: The FM-index is a celebrated compressed data structure for full-text pattern searching. After the first wave of interest in its theoretical developments, we can observe a surge of interest in practical FM-index variants in the last few years. These enhancements are often related to a bit-vector representation, augmented with an efficient rank-handling data structure. In this work, we propose a new, cache-friendly, implementation of the rank primitive and advocate for a very simple architecture of the FM-index, which trades compression ratio for speed. Experimental results show that our variants are 2--3 times faster than the fastest known ones, for the price of using typically 1.5--5 times more space.

Includes members:
  Member "space efficient construction of compressed indexes in deterministic linear time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering")
  Member "two simple full text indexes based on the suffix array":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sampling the suffix array with minimizers")
  Member "sampling the suffix array with minimizers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient online string matching based on characters distance text sampling")
  Member "fixed block compression boosting in fm indexes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressed multiple pattern matching")
  Member "compressed text indexes from theory to practice":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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)