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: "in place longest common extensions"
  Abstract: Longest Common Extension (LCE) queries are a fundamental sub-routine in several string-processing algorithms, including (but not limited to) suffix-sorting, string matching, compression, and identification of repeats and palindrome factors. A LCE query takes as input two positions $i,j$ in a text $T\in\Sigma^n$ and returns the length $\ell$ of the longest common prefix between $T$'s $i$-th and $j$-th suffixes. In this paper, we present the following result: we can replace the (plain) text with a data structure of the \emph{exact same size}---$n\lceil\log_2|\Sigma|\rceil$ bits---supporting text extraction in optimal time and LCE queries in logarithmic time---i.e. \emph{exponentially} faster than what can be achieved using the plain text alone. Our structure can be built in $\mathcal O(n\log n)$ expected time and linear space. We show that our result is a powerful tool that can be used to efficiently solve in-place a wide variety of string processing problems: we provide the first in-place algorithms to compute the LCP array in $\mathcal O(n\log n)$ expected time (the previous fastest in-place algorithm runs in $\mathcal O(n^2)$ time) and to suffix-sort---with high probability of success---any set of $b$ text suffixes in $\mathcal O(n+b\log^2 n)$ expected time (the previous fastest in-place algorithm runs in $\mathcal O(nb)$ time).

Connected through hyperedge HE-1 (induced by source paper "burrows wheeler transform and lcp array construction in constant space"):
  with members:
    - "linear time string indexing and analysis in small space"
    - "low space external memory construction of the succinct permuted longest common prefix array"
    - "faster average case low memory semi external construction of the burrows wheeler transform"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "lightweight lcp construction for very large collections of strings"
    - "linear time construction of compressed text indices in compact space"
    - "lightweight lcp construction for next generation sequencing datasets"
    - "optimal dynamic sequence representations"

Connected through hyperedge HE-2 (induced by source paper "lightweight fingerprints for fast approximate keyword matching using bitwise operations"):
  with members:
    - "fingerprints in compressed strings"

Connected through hyperedge HE-3 (induced by source paper "tight lower bounds for the longest common extension problem"):
  with members:
    - "small space encoding lce data structure with constant time queries"
    - "fast longest common extensions in small space"
    - "faster longest common extension queries in strings over general alphabets"
    - "sparse suffix tree construction in optimal time and space"
    - "longest common extensions with recompression"
    - "deterministic sub linear space lce data structures with efficient construction"
    - "longest common extensions in sublinear space"
    - "computing runs on a general alphabet"

Connected through hyperedge HE-4 (induced by source paper "longest common extensions with recompression"):
  with members:
    - "finger search in grammar compressed strings"
    - "fingerprints in compressed strings"
    - "approximation of grammar based compression via recompression"
    - "approximation of smallest linear tree grammar"
    - "one variable word equations in linear time"
    - "recompression a simple and powerful technique for word equations"
    - "faster fully compressed pattern matching by recompression"

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)