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 "fully functional suffix trees and optimal text searching in bwt runs bounded space"):
  Abstract: Indexing highly repetitive texts --- such as genomic databases, software repositories and versioned text collections --- has become an important problem since the turn of the millennium. A relevant compressibility measure for repetitive texts is $r$, the number of runs in their Burrows-Wheeler Transforms (BWTs). One of the earliest indexes for repetitive collections, the Run-Length FM-index, used $O(r)$ space and was able to efficiently count the number of occurrences of a pattern of length $m$ in the text (in loglogarithmic time per pattern symbol, with current techniques). However, it was unable to locate the positions of those occurrences efficiently within a space bounded in terms of$r$. Since then, a number of other indexes with space bounded by other measures of repetitiveness --- the number of phrases in the Lempel-Ziv parse, the sizeof the smallest grammar generating (only) the text, the size of the smallest automaton recognizing the text factors --- have been proposed for efficiently locating,but not directly counting, the occurrences of a pattern. In this paper we close this long-standing problem, showing how to extend the Run-Length FM-index so that it can locate the $occ$ occurrences efficiently within $O(r)$ space (in loglogarithmic time each), and reaching optimal time $O(m+occ)$ within $O(r\log(n/r))$ space, on a RAM machine with words of $w=\Omega(\log n)$ bits. Within $O(r\log (n/r))$ space, our index can also count in optimal time $O(m)$.Raising the space to $O(r w\log_\sigma(n/r))$, we support count and locate in $O(\lceil m\log(\sigma)/w\rceil)$ and $O(\lceil m\log(\sigma)/w\rceil+occ)$ time, which is optimal in the packed setting and had not been obtained before in compressed space. We also describe a structure using $O(r\log(n/r))$ space that replaces the text and extracts any text substring ...

Includes members:
  Member "resolution of the burrows wheeler transform conjecture":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on extensions of maximal repeats in compressed strings")
  Member "faster repetition aware compressed suffix trees based on block trees":
    also connected through other hyperedges:
      (none)
  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 "fast prefix search in little space with applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal compressed text indexing")
  Member "efficient construction of a complete index for pan genomics read alignment":
    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 approximation ratio of greedy parsings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards a definitive measure of repetitiveness")
  Member "universal compressed text indexing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "top tree compression of tries")
  Member "prefix free parsing for building big bwts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rpair rescaling repair with rsync")

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)