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 "optimal construction of compressed indexes for highly repetitive texts"):
  Abstract: We propose algorithms that, given the input string of length $n$ over integer alphabet of size $\sigma$, construct the Burrows-Wheeler transform (BWT), the permuted longest-common-prefix (PLCP) array, and the LZ77 parsing in $O(n/\log_{\sigma}n+r\,{\rm polylog}\,n)$ time and working space, where $r$ is the number of runs in the BWT of the input. These are the essential components of many compressed indexes such as compressed suffix tree, FM-index, and grammar and LZ77-based indexes, but also find numerous applications in sequence analysis and data compression. The value of $r$ is a common measure of repetitiveness that is significantly smaller than $n$ if the string is highly repetitive. Since just accessing every symbol of the string requires $\Omega(n/\log_{\sigma}n)$ time, the presented algorithms are time and space optimal for inputs satisfying the assumption $n/r\in\Omega({\rm polylog}\,n)$ on the repetitiveness. For such inputs our result improves upon the currently fastest general algorithms of Belazzougui (STOC 2014) and Munro et al. (SODA 2017) which run in $O(n)$ time and use $O(n/\log_{\sigma} n)$ working space. We also show how to use our techniques to obtain optimal solutions on highly repetitive data for other fundamental string processing problems such as: Lyndon factorization, construction of run-length compressed suffix arrays, and some classical "textbook" problems such as computing the longest substring occurring at least some fixed number of times.

Includes members:
  Member "time space trade offs for lempel ziv compressed indexing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "top tree compression of tries")
  Member "at the roots of dictionary compression string attractors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards a definitive measure of repetitiveness")
  Member "optimal time text indexing in bwt runs bounded space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast indexes for gapped pattern matching")
  Member "space efficient construction of compressed indexes in deterministic linear time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering")
  Member "on the size of lempel ziv and lyndon factorizations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lyndon words versus inverse lyndon words queries on suffixes and bordered words")
  Member "linear time construction of compressed text indices in compact space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering")
  Member "queries on lz bounded encodings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "top tree compression of tries")
  Member "approximation of grammar based compression via recompression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on string constraint solving")

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)