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 "finger search in grammar compressed strings"):
  Abstract: Grammar-based compression, where one replaces a long string by a small context-free grammar that generates the string, is a simple and powerful paradigm that captures many popular compression schemes. Given a grammar, the random access problem is to compactly represent the grammar while supporting random access, that is, given a position in the original uncompressed string report the character at that position. In this paper we study the random access problem with the finger search property, that is, the time for a random access query should depend on the distance between a specified index $f$, called the \emph{finger}, and the query index $i$. We consider both a static variant, where we first place a finger and subsequently access indices near the finger efficiently, and a dynamic variant where also moving the finger such that the time depends on the distance moved is supported. #R##N#Let $n$ be the size the grammar, and let $N$ be the size of the string. For the static variant we give a linear space representation that supports placing the finger in $O(\log N)$ time and subsequently accessing in $O(\log D)$ time, where $D$ is the distance between the finger and the accessed index. For the dynamic variant we give a linear space representation that supports placing the finger in $O(\log N)$ time and accessing and moving the finger in $O(\log D + \log \log N)$ time. Compared to the best linear space solution to random access, we improve a $O(\log N)$ query bound to $O(\log D)$ for the static variant and to $O(\log D + \log \log N)$ for the dynamic variant, while maintaining linear space. As an application of our results we obtain an improved solution to the longest common extension problem in grammar compressed strings. To obtain our results, we introduce several new techniques of independent interest, including a novel van Emde Boas style decomposition of grammars.

Includes members:
  Member "fully dynamic data structure for lce queries in compressed space":
    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 "dynamic index lz factorization and lce queries in compressed space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rpair rescaling repair with rsync")
  Member "queries on lz bounded encodings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "top tree compression of tries")
  Member "compressed subsequence matching and packed tree coloring":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "balancing straight line programs")
  Member "computing convolution on grammar compressed text":
    also connected through other hyperedges:
      (none)
  Member "fingerprints in compressed strings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "local decodability of the burrows wheeler transform")
  Member "data structure lower bounds on random access to grammar compressed strings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space")
  Member "a faster grammar based self index":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "top tree compression of tries")

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)