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 time dictionary compressed indexes"):
  Abstract: We describe the first self-indexes able to count and locate pattern occurrences in optimal time within a space bounded by the size of the most popular dictionary compressors. To achieve this result we combine several recent findings, including \emph{string attractors} --- new combinatorial objects encompassing most known compressibility measures for highly repetitive texts ---, and grammars based on \emph{locally-consistent parsing}. #R##N#More in detail, let $\gamma$ be the size of the smallest attractor for a text $T$ of length $n$. The measure $\gamma$ is an (asymptotic) lower bound to the size of dictionary compressors based on Lempel--Ziv, context-free grammars, and many others. The smallest known text representations in terms of attractors use space $O(\gamma\log(n/\gamma))$, and our lightest indexes work within the same asymptotic space. Let $\epsilon>0$ be a suitably small constant fixed at construction time, $m$ be the pattern length, and $occ$ be the number of its text occurrences. Our index counts pattern occurrences in $O(m+\log^{2+\epsilon}n)$ time, and locates them in $O(m+(occ+1)\log^\epsilon n)$ time. These times already outperform those of most dictionary-compressed indexes, while obtaining the least asymptotic space for any index searching within $O((m+occ)\,\textrm{polylog}\,n)$ time. Further, by increasing the space to $O(\gamma\log(n/\gamma)\log^\epsilon n)$, we reduce the locating time to the optimal $O(m+occ)$, and within $O(\gamma\log(n/\gamma)\log n)$ space we can also count in optimal $O(m)$ time. No dictionary-compressed index had obtained this time before. All our indexes can be constructed in $O(n)$ space and $O(n\log n)$ expected time. #R##N#As a byproduct of independent interest...

Includes members:
  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 "fast prefix search in little space with applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space")
  Member "universal compressed text indexing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "top tree compression of tries")
  Member "representing the suffix tree with the cdawg":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards a definitive measure of repetitiveness")
  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 "at the roots of dictionary compression string attractors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards a definitive measure of repetitiveness")
  Member "compressed indexing with signature grammars":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "top tree compression of tries")
  Member "document listing on repetitive collections with guaranteed performance":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast small and simple document listing on repetitive text collections")

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)