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 "document listing on repetitive collections with guaranteed performance"):
  Abstract: We consider document listing on string collections, that is, finding in which strings a given pattern appears. In particular, we focus on repetitive collections: a collection of size $N$ over alphabet $[1,\sigma]$ is composed of $D$ copies of a string of size $n$, and $s$ edits are applied on ranges of copies. We introduce the first document listing index with size $\tilde{O}(n+s)$, precisely $O((n\log\sigma+s\log^2 N)\log D)$ bits, and with useful worst-case time guarantees: Given a pattern of length $m$, the index reports the $\ndoc>0$ strings where it appears in time $O(m\log^{1+\epsilon} N \cdot \ndoc)$, for any constant $\epsilon>0$ (and tells in time $O(m\log N)$ if $\ndoc=0$). Our technique is to augment a range data structure that is commonly used on grammar-based indexes, so that instead of retrieving all the pattern occurrences, it computes useful summaries on them. We show that the idea has independent interest: we introduce the first grammar-based index that, on a text $T[1,N]$ with a grammar of size $r$, uses $O(r\log N)$ bits and counts the number of occurrences of a pattern $P[1,m]$ in time $O(m^2 + m\log^{2+\epsilon} r)$, for any constant $\epsilon>0$. We also give the first index using $O(z\log(N/z)\log N)$ bits, where $T$ is parsed by Lempel-Ziv into $z$ phrases, counting occurrences in time $O(m\log^{2+\epsilon} N)$.

Includes members:
  Member "fast prefix search in little space with applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "top tree compression of tries")
  Member "compressed indexing with signature grammars":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "top tree compression of tries")
  Member "optimal time text indexing in bwt runs bounded space":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast indexes for gapped pattern matching")
  Member "document retrieval on repetitive string collections":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast small and simple document listing on repetitive text collections")
  Member "fully dynamic data structure for lce queries in compressed space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space")
  Member "composite repetition aware data structures":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on extensions of maximal repeats in compressed strings")

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)