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 "towards a definitive measure of repetitiveness"):
  Abstract: Unlike in statistical compression, where Shannon's entropy is a definitive lower bound, no such a clear measure exists for the compressibility of repetitive sequences other than the uncomputable Kolmogorov's complexity. Since statistical entropy does not capture repetitiveness, ad-hoc measures like the size $z$ of the Lempel-Ziv parse are frequently used to estimate it. Recently, a more principled measure, the size $\gamma$ of the smallest {\em attractor} of a string $S[1..n]$, was introduced. Measure $\gamma$ lower bounds all the previous relevant ones (e.g., $z$), yet $S$ can be represented and indexed within space $O(\gamma\log(n/\gamma))$, which also upper bounds most measures. While $\gamma$ is certainly a better measure of repetitiveness, it is NP-complete to compute, and it is not known if $S$ can always be represented in $O(\gamma)$ space. #R##N#In this paper we study a smaller measure, $\delta \le \gamma$, which can be computed in linear time. We show that $\delta$ captures better the concept of compressibility in repetitive strings: We prove that, for some string families, it holds $\gamma = \Omega(\delta \log n)$. Still, we can build a representation of $S$ of size $O(\delta\log(n/\delta))$, which supports direct access to any $S[i]$ in time $O(\log(n/\delta))$ and finds the $occ$ occurrences of any pattern $P[1..m]$ in time $O(m\log n + occ\log^\epsilon n)$ for any constant $\epsilon>0$. Further, such representation is worst-case optimal because, in some families, $S$ can only be represented in $\Omega(\delta\log n)$ space. We complete our characterization of $\delta$ by showing that $\gamma$, $z$ and other measures of repetitiveness are always $O(\delta\log(n/\delta))$, but in some string families, the smallest context-free grammar is of size $g=\Omega(\delta \log^2 n / \log\log n)$. No such a lower bound is known to hold for $\gamma$.

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 "optimal rank and select queries on dictionary compressed text":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resolution of the burrows wheeler transform conjecture")
  Member "optimal time dictionary compressed indexes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "resolution of the burrows wheeler transform conjecture")
  Member "fully functional suffix trees and optimal text searching in bwt runs bounded space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "resolution of the burrows wheeler transform conjecture")
  Member "on the approximation ratio of greedy parsings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "top tree compression of tries")
  Member "representing the suffix tree with the cdawg":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fully functional bidirectional burrows wheeler indexes")
  Member "time space trade offs for lempel ziv compressed indexing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)