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: "faster approximate pattern matching in compressed repetitive texts"
  Abstract: Motivated by the imminent growth of massive, highly redundant genomic databases, we study the problem of compressing a string database while simultaneously supporting fast random access, substring extraction and pattern matching to the underlying string(s). Bille et al. (2011) recently showed how, given a straight-line program with $r$ rules for a string $s$ of length $n$, we can build an $\Oh{r}$-word data structure that allows us to extract any substring of length $m$ in $\Oh{\log n + m}$ time. They also showed how, given a pattern $p$ of length $m$ and an edit distance (k \leq m), their data structure supports finding all \occ approximate matches to $p$ in $s$ in $\Oh{r (\min (m k, k^4 + m) + \log n) + \occ}$ time. Rytter (2003) and Charikar et al. (2005) showed that $r$ is always at least the number $z$ of phrases in the LZ77 parse of $s$, and gave algorithms for building straight-line programs with $\Oh{z \log n}$ rules. In this paper we give a simple $\Oh{z \log n}$-word data structure that takes the same time for substring extraction but only $\Oh{z \min (m k, k^4 + m) + \occ}$ time for approximate pattern matching.

Connected through hyperedge HE-1 (induced by source paper "fine grained complexity of analyzing compressed data quantifying improvements over decompress and solve"):
  with members:
    - "truly sub cubic algorithms for language edit distance and rna folding via fast bounded difference min plus product"
    - "why is it hard to beat o n 2 for longest common weakly increasing subsequence"
    - "a dichotomy for regular expression membership testing"
    - "conditional lower bound for rna folding problem"
    - "the k mismatch problem revisited"
    - "clustered integer 3sum via additive combinatorics"
    - "which regular expression patterns are hard to match"
    - "simulating branching programs with edit distance and friends or a polylog shaved is a lower bound made"

Connected through hyperedge HE-2 (induced by source paper "lempel ziv decoding in external memory"):
  with members:
    - "hybrid indexes for repetitive datasets"
    - "lempel ziv parsing in external memory"
    - "relative lempel ziv factorization for efficient storage and retrieval of web collections"
    - "grammar based compression in a streaming model"

Connected through hyperedge HE-3 (induced by source paper "searching and indexing genomic databases via kernelization"):
  with members:
    - "fully online grammar compression in constant space"
    - "hybrid indexes for repetitive datasets"
    - "improved esp index a practical self index for highly repetitive texts"
    - "indexes of large genome collections on a pc"
    - "data structure lower bounds on random access to grammar compressed strings"
    - "improved grammar based compressed indexes"
    - "random access to grammar compressed strings"

Connected through hyperedge HE-4 (induced by source paper "queries on lz bounded encodings"):
  with members:
    - "rank select and access in grammar compressed strings"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-5 (induced by source paper "hybrid indexes for repetitive datasets"):
  with members:
    - "improved grammar based compressed indexes"
    - "a faster grammar based self index"

Connected through hyperedge HE-6 (induced by source paper "linear time lempel ziv factorization simple fast small"):
  with members:
    - "lightweight lempel ziv parsing"
    - "simpler and faster lempel ziv factorization"
    - "a faster grammar based self index"

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)