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 "resolution of the burrows wheeler transform conjecture"):
  Abstract: Burrows-Wheeler Transform (BWT) is an invertible text transformation that permutes symbols of a text according to the lexicographical order of its suffixes. BWT is the main component of some of the most popular lossless compression methods as well as of compressed indexes, central in modern bioinformatics. The compression ratio of BWT-based compressors, such as bzip2, is quantified by the number $r$ of maximal equal-letter runs in the BWT. This is also (up to ${\rm polylog}\,n$ factors, where $n$ is the length of the text) the space used by the state-of-the-art BWT-based indexes, such as the recent $r$-index [Gagie et al., SODA 2018]. The output size of virtually every known compression method is known to be either within a ${\rm polylog}\,n$ factor from $z$, the size of Lempel-Ziv (LZ77) parsing of the text, or significantly larger (by a $n^{\epsilon}$ factor for $\epsilon > 0$). The value of $r$ has resisted, however, all attempts and until now, no non-trivial upper bounds on $r$ were known. #R##N#In this paper, we show that every text satisfies $r=\mathcal{O}(z\log^2 n)$. This result has a number of immediate implications: (1) it proves that a large body of work related to BWT automatically applies to the so-far disjoint field of Lempel--Ziv indexing and compression, e.g., it is possible to obtain full functionality of the suffix tree and the suffix array in $\mathcal{O}(z\,{\rm polylog}\,n)$ space; (2) it lets us relate the number of runs in the BWT of the text and its reverse; (3) it shows that many fundamental text processing tasks can be solved in the optimal time assuming that the text is compressible by a sufficiently large ${\rm polylog}\,n$ factor using LZ77.

Includes members:
  Member "towards a definitive measure of repetitiveness":
    also connected through other hyperedges:
      (none)
  Member "optimal rank and select queries on dictionary compressed text":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards a definitive measure of repetitiveness")
  Member "optimal time dictionary compressed indexes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards a definitive measure of repetitiveness")
  Member "fully functional suffix trees and optimal text searching in bwt runs bounded space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards a definitive measure of repetitiveness")
  Member "universal compressed text indexing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "top tree compression of tries")
  Member "time space trade offs for lempel ziv compressed indexing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "top tree compression of tries")
  Member "optimal construction of compressed indexes for highly repetitive texts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering")
  Member "at the roots of dictionary compression string attractors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards a definitive measure of repetitiveness")

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)