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 "rank select and access in grammar compressed strings"):
  Abstract: Given a string $S$ of length $N$ on a fixed alphabet of $\sigma$ symbols, a grammar compressor produces a context-free grammar $G$ of size $n$ that generates $S$ and only $S$. In this paper we describe data structures to support the following operations on a grammar-compressed string: $\mbox{rank}_c(S,i)$ (return the number of occurrences of symbol $c$ before position $i$ in $S$); $\mbox{select}_c(S,i)$ (return the position of the $i$th occurrence of $c$ in $S$); and $\mbox{access}(S,i,j)$ (return substring $S[i,j]$). For rank and select we describe data structures of size $O(n\sigma\log N)$ bits that support the two operations in $O(\log N)$ time. We propose another structure that uses $O(n\sigma\log (N/n)(\log N)^{1+\epsilon})$ bits and that supports the two queries in $O(\log N/\log\log N)$, where $\epsilon>0$ is an arbitrary constant. To our knowledge, we are the first to study the asymptotic complexity of rank and select in the grammar-compressed setting, and we provide a hardness result showing that significantly improving the bounds we achieve would imply a major breakthrough on a hard graph-theoretical problem. Our main result for access is a method that requires $O(n\log N)$ bits of space and $O(\log N+m/\log_\sigma N)$ time to extract $m=j-i+1$ consecutive symbols from $S$. Alternatively, we can achieve $O(\log N/\log\log N+m/\log_\sigma N)$ query time using $O(n\log (N/n)(\log N)^{1+\epsilon})$ bits of space. This matches a lower bound stated by Verbin and Yu for strings where $N$ is polynomially related to $n$.

Includes members:
  Member "compressed subsequence matching and packed tree coloring":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "balancing straight line programs")
  Member "compact q gram profiling of compressed strings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "practical repetition aware grammar compression")
  Member "spaces trees and colors the algorithmic landscape of document retrieval on sequences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast small and simple document listing on repetitive text collections")
  Member "data structure lower bounds on random access to grammar compressed strings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space")
  Member "improved grammar based compressed indexes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "top tree compression of tries")
  Member "fast q gram mining on slp compressed strings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressed range minimum queries")
  Member "unified compression based acceleration of edit distance computation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fine grained complexity of analyzing compressed data quantifying improvements over decompress and solve")
  Member "random access to grammar compressed strings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "local decode and update for big data compression")

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)