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: "optimal time text indexing in bwt runs bounded space"
  Abstract: Indexing highly repetitive texts --- such as genomic databases, software repositories and versioned text collections --- has become an important problem since the turn of the millennium. A relevant compressibility measure for repetitive texts is $r$, the number of runs in their Burrows-Wheeler Transform (BWT). One of the earliest indexes for repetitive collections, the Run-Length FM-index, used $O(r)$ space and was able to efficiently count the number of occurrences of a pattern of length $m$ in the text (in loglogarithmic time per pattern symbol, with current techniques). However, it was unable to locate the positions of those occurrences efficiently within a space bounded in terms of $r$. Since then, a number of other indexes with space bounded by other measures of repetitiveness --- the number of phrases in the Lempel-Ziv parse, the size of the smallest grammar generating the text, the size of the smallest automaton recognizing the text factors --- have been proposed for efficiently locating, but not directly counting, the occurrences of a pattern. In this paper we close this long-standing problem, showing how to extend the Run-Length FM-index so that it can locate the $occ$ occurrences efficiently within $O(r)$ space (in loglogarithmic time each), and reaching optimal time $O(m+occ)$ within $O(r\log(n/r))$ space, on a RAM machine of $w=\Omega(\log n)$ bits. Within $O(r\log (n/r))$ space, our index can also count in optimal time $O(m)$. Raising the space to $O(r w\log_\sigma(n/r))$, we support count and locate in $O(m\log(\sigma)/w)$ and $O(m\log(\sigma)/w+occ)$ time, which is optimal in the packed setting and had not been obtained before in compressed space. We also describe a structure using $O(r\log(n/r))$ space that replaces the text and extracts any text substring of length $\ell$ in almost-optimal time $O(\log(n/r)+\ell\log(\sigma)/w)$. (...continues...)

Connected through hyperedge HE-1 (induced by source paper "fast indexes for gapped pattern matching"):
  with members:
    - "fast and compact regular expression matching"

Connected through hyperedge HE-2 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering"):
  with members:
    - "on the hardness and inapproximability of recognizing wheeler graphs"
    - "resolution of the burrows wheeler transform conjecture"
    - "string synchronizing sets sublinear time bwt construction and optimal lce data structure"
    - "tunneling on wheeler graphs"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "optimal construction of compressed indexes for highly repetitive texts"
    - "at the roots of dictionary compression string attractors"
    - "space efficient construction of compressed indexes in deterministic linear time"

Connected through hyperedge HE-3 (induced by source paper "practical repetition aware grammar compression"):
  with members:
    - "mr repair grammar compression based on maximal repeats"
    - "repair in compressed space and time"
    - "scalable partial least squares regression on grammar compressed data matrices"
    - "space efficient re pair compression"
    - "online pattern matching for string edit distance with moves"
    - "compact q gram profiling of compressed strings"
    - "a succinct grammar compression"
    - "approximation of grammar based compression via recompression"

Connected through hyperedge HE-4 (induced by source paper "fast small and simple document listing on repetitive text collections"):
  with members:
    - "fully functional suffix trees and optimal text searching in bwt runs bounded space"
    - "document listing on repetitive collections with guaranteed performance"
    - "document retrieval on repetitive string collections"
    - "document retrieval on repetitive collections"
    - "a really simple approximation of smallest grammar"
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"

Connected through hyperedge HE-5 (induced by source paper "regular languages meet prefix sorting"):
  with members:
    - "on the hardness and inapproximability of recognizing wheeler graphs"
    - "which regular expression patterns are hard to match"
    - "optimal dynamic sequence representations"
    - "improved grammar based compressed indexes"

Connected through hyperedge HE-6 (induced by source paper "a new class of searchable and provably highly compressible string transformations"):
  with members:
    - "at the roots of dictionary compression string attractors"
    - "large scale compression of genomic sequence databases with the burrows wheeler transform"
    - "optimal lower and upper bounds for representing sequences"
    - "a note on the burrows wheeler transformation"

Connected through hyperedge HE-7 (induced by source paper "faster repetition aware compressed suffix trees based on block trees"):
  with members:
    - "representing the suffix tree with the cdawg"
    - "queries on lz bounded encodings"
    - "fully functional static and dynamic succinct trees"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-8 (induced by source paper "constructing the bijective bwt"):
  with members:
    - "on generalized lyndon words"
    - "a bijective string sorting transform"
    - "on bijective variants of the burrows wheeler transform"

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)