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 dictionary compressed indexes"
  Abstract: We describe the first self-indexes able to count and locate pattern occurrences in optimal time within a space bounded by the size of the most popular dictionary compressors. To achieve this result we combine several recent findings, including \emph{string attractors} --- new combinatorial objects encompassing most known compressibility measures for highly repetitive texts ---, and grammars based on \emph{locally-consistent parsing}. #R##N#More in detail, let $\gamma$ be the size of the smallest attractor for a text $T$ of length $n$. The measure $\gamma$ is an (asymptotic) lower bound to the size of dictionary compressors based on Lempel--Ziv, context-free grammars, and many others. The smallest known text representations in terms of attractors use space $O(\gamma\log(n/\gamma))$, and our lightest indexes work within the same asymptotic space. Let $\epsilon>0$ be a suitably small constant fixed at construction time, $m$ be the pattern length, and $occ$ be the number of its text occurrences. Our index counts pattern occurrences in $O(m+\log^{2+\epsilon}n)$ time, and locates them in $O(m+(occ+1)\log^\epsilon n)$ time. These times already outperform those of most dictionary-compressed indexes, while obtaining the least asymptotic space for any index searching within $O((m+occ)\,\textrm{polylog}\,n)$ time. Further, by increasing the space to $O(\gamma\log(n/\gamma)\log^\epsilon n)$, we reduce the locating time to the optimal $O(m+occ)$, and within $O(\gamma\log(n/\gamma)\log n)$ space we can also count in optimal $O(m)$ time. No dictionary-compressed index had obtained this time before. All our indexes can be constructed in $O(n)$ space and $O(n\log n)$ expected time. #R##N#As a byproduct of independent interest...

Connected through hyperedge HE-1 (induced by source paper "towards a definitive measure of repetitiveness"):
  with members:
    - "resolution of the burrows wheeler transform conjecture"
    - "optimal rank and select queries on dictionary compressed text"
    - "fully functional suffix trees and optimal text searching in bwt runs bounded space"
    - "on the approximation ratio of greedy parsings"
    - "universal compressed text indexing"
    - "representing the suffix tree with the cdawg"
    - "time space trade offs for lempel ziv compressed indexing"
    - "at the roots of dictionary compression string attractors"

Connected through hyperedge HE-2 (induced by source paper "resolution of the burrows wheeler transform conjecture"):
  with members:
    - "towards a definitive measure of repetitiveness"
    - "optimal rank and select queries on dictionary compressed text"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space"):
  with members:
    - "resolution of the burrows wheeler transform conjecture"
    - "faster repetition aware compressed suffix trees based on block trees"
    - "fast prefix search in little space with applications"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "on the approximation ratio of greedy parsings"
    - "universal compressed text indexing"
    - "prefix free parsing for building big bwts"
    - "representing the suffix tree with the cdawg"

Connected through hyperedge HE-4 (induced by source paper "on the approximation ratio of greedy parsings"):
  with members:
    - "at the roots of dictionary compression string attractors"
    - "compression with the tudocomp framework"
    - "optimal time text indexing in bwt runs bounded space"
    - "a separation between run length slps and lz77"
    - "fully dynamic data structure for lce queries in compressed space"
    - "composite repetition aware data structures"
    - "factorizations of the fibonacci infinite word"
    - "queries on lz bounded encodings"

Connected through hyperedge HE-5 (induced by source paper "a compressed dynamic self index for highly repetitive text collections"):
  with members:
    - "universal compressed text indexing"
    - "small space encoding lce data structure with constant time queries"
    - "time space trade offs for lempel ziv compressed indexing"
    - "at the roots of dictionary compression string attractors"
    - "compressed indexing with signature grammars"
    - "optimal time text indexing in bwt runs bounded space"
    - "a self index on block trees"
    - "dynamic index and lz factorization in compressed space"

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)