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 "linear size cdawg new repetition aware indexing and grammar compression"):
  Abstract: In this paper, we propose a novel approach to combine \emph{compact directed acyclic word graphs} (CDAWGs) and grammar-based compression. This leads us to an efficient self-index, called Linear-size CDAWGs (L-CDAWGs), which can be represented with $O(\tilde e_T \log n)$ bits of space allowing for $O(\log n)$-time random and $O(1)$-time sequential accesses to edge labels, and $O(m \log \sigma + occ)$-time pattern matching. Here, $\tilde e_T$ is the number of all extensions of maximal repeats in $T$, $n$ and $m$ are respectively the lengths of the text $T$ and a given pattern, $\sigma$ is the alphabet size, and $occ$ is the number of occurrences of the pattern in $T$. The repetitiveness measure $\tilde e_T$ is known to be much smaller than the text length $n$ for highly repetitive text. For constant alphabets, our L-CDAWGs achieve $O(m + occ)$ pattern matching time with $O(e_T^r \log n)$ bits of space, which improves the pattern matching time of Belazzougui et al.'s run-length BWT-CDAWGs by a factor of $\log \log n$, with the same space complexity. Here, $e_T^r$ is the number of right extensions of maximal repeats in $T$. As a byproduct, our result gives a way of constructing an SLP of size $O(\tilde e_T)$ for a given text $T$ in $O(n + \tilde e_T \log \sigma)$ time.

Includes members:
  Member "a self index on block trees":
    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 "composite repetition aware data structures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on extensions of maximal repeats in compressed strings")
  Member "improved esp index a practical self index for highly repetitive texts":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a compressed dynamic self index for highly repetitive text collections")

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)