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: "fast label extraction in the cdawg"
  Abstract: The compact directed acyclic word graph (CDAWG) of a string $T$ of length $n$ takes space proportional just to the number $e$ of right extensions of the maximal repeats of $T$, and it is thus an appealing index for highly repetitive datasets, like collections of genomes from similar species, in which $e$ grows significantly more slowly than $n$. We reduce from $O(m\log{\log{n}})$ to $O(m)$ the time needed to count the number of occurrences of a pattern of length $m$, using an existing data structure that takes an amount of space proportional to the size of the CDAWG. This implies a reduction from $O(m\log{\log{n}}+\mathtt{occ})$ to $O(m+\mathtt{occ})$ in the time needed to locate all the $\mathtt{occ}$ occurrences of the pattern. We also reduce from $O(k\log{\log{n}})$ to $O(k)$ the time needed to read the $k$ characters of the label of an edge of the suffix tree of $T$, and we reduce from $O(m\log{\log{n}})$ to $O(m)$ the time needed to compute the matching statistics between a query of length $m$ and $T$, using an existing representation of the suffix tree based on the CDAWG. All such improvements derive from extracting the label of a vertex or of an arc of the CDAWG using a straight-line program induced by the reversed CDAWG.

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"
    - "optimal time dictionary compressed indexes"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "fully functional bidirectional burrows wheeler indexes"):
  with members:
    - "simulating the dna string graph in succinct space"
    - "assembling omnitigs using hidden order de bruijn graphs"
    - "representing the suffix tree with the cdawg"
    - "linear time string indexing and analysis in small space"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "linear time construction of compressed text indices in compact space"
    - "variable order de bruijn graphs"
    - "engineering small space dictionary matching"

Connected through hyperedge HE-3 (induced by source paper "suffix trees dawgs and cdawgs for forward and backward tries"):
  with members:
    - "linear size cdawg new repetition aware indexing and grammar compression"
    - "alphabet dependent string searching with wexponential search trees"
    - "faster compact on line lempel ziv factorization"
    - "fast computation of subpath kernel for trees"

Connected through hyperedge HE-4 (induced by source paper "online algorithms for constructing linear size suffix trie"):
  with members:
    - "linear size cdawg new repetition aware indexing and grammar compression"
    - "optimal dynamic strings"
    - "alphabet dependent string searching with wexponential search trees"
    - "on line construction of position heaps"

Connected through hyperedge HE-5 (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"
    - "optimal time dictionary compressed indexes"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "universal compressed text indexing"):
  with members:
    - "fast prefix search in little space with applications"
    - "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"
    - "sparse suffix tree construction in optimal time and space"
    - "a self index on block trees"
    - "dynamic index and lz factorization in compressed space"

Connected through hyperedge HE-7 (induced by source paper "mr repair grammar compression based on maximal repeats"):
  with members:
    - "representing the suffix tree with the cdawg"
    - "linear size cdawg new repetition aware indexing and grammar compression"
    - "scalable partial least squares regression on grammar compressed data matrices"
    - "space efficient re pair compression"
    - "composite repetition aware data structures"

Connected through hyperedge HE-8 (induced by source paper "the colored longest common prefix array computed via sequential scans"):
  with members:
    - "external memory bwt and lcp computation for sequence collections with applications"
    - "lightweight lcp construction for very large collections of strings"

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)