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 "fast longest common extensions in small space"):
  Abstract: In this paper we address the longest common extension (LCE) problem: to compute the length $\ell$ of the longest common prefix between any two suffixes of $T\in \Sigma^n$ with $ \Sigma = \{0, \ldots \sigma-1\} $. We present two fast and space-efficient solutions based on (Karp-Rabin) \textit{fingerprinting} and \textit{sampling}. Our first data structure exploits properties of Mersenne prime numbers when used as moduli of the Karp-Rabin hash function and takes $n\lceil \log_2\sigma\rceil$ bits of space. Our second structure works with any prime modulus and takes $n\lceil \log_2\sigma\rceil + n/w + w\log_2 n$ bits of space ($ w $ memory-word size). Both structures support $\mathcal O\left(m\log\sigma/w \right)$-time extraction of any length-$m$ text substring, $\mathcal O(\log\ell)$-time LCE queries with high probability, and can be built in optimal $\mathcal O(n)$ time. In the first case, ours is the first result showing that it is possible to answer LCE queries in $o(n)$ time while using only $\mathcal O(1)$ words on top of the space required to store the text. Our results improve the state of the art in space usage, query times, and preprocessing times and are extremely practical: we present a C++ implementation that is very fast and space-efficient in practice.

Includes members:
  Member "dynamic index lz factorization and lce queries in compressed space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rpair rescaling repair with rsync")
  Member "longest common extensions in sublinear space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "string indexing with compressed patterns")
  Member "searching of gapped repeats and subrepetitions in a word":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matching patterns with variables")

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)