human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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 $\gamma$.

Connected through hyperedge HE-1 (induced by source paper "resolution of the burrows wheeler transform conjecture"):
  with members:
    - "optimal rank and select queries on dictionary compressed text"
    - "optimal time dictionary compressed indexes"
    - "fully functional suffix trees and optimal text searching in bwt runs bounded space"
    - "universal compressed text indexing"
    - "time space trade offs for lempel ziv compressed indexing"
    - "optimal construction of compressed indexes for highly repetitive texts"
    - "at the roots of dictionary compression string attractors"
    - "queries on lz bounded encodings"

Question: Please tell me which class the center node 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)