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: "longest common extensions with recompression"
  Abstract: Given two positions $i$ and $j$ in a string $T$ of length $N$, a longest common extension (LCE) query asks for the length of the longest common prefix between suffixes beginning at $i$ and $j$. A compressed LCE data structure is a data structure that stores $T$ in a compressed form while supporting fast LCE queries. In this article we show that the recompression technique is a powerful tool for compressed LCE data structures. We present a new compressed LCE data structure of size $O(z \lg (N/z))$ that supports LCE queries in $O(\lg N)$ time, where $z$ is the size of Lempel-Ziv 77 factorization without self-reference of $T$. Given $T$ as an uncompressed form, we show how to build our data structure in $O(N)$ time and space. Given $T$ as a grammar compressed form, i.e., an straight-line program of size n generating $T$, we show how to build our data structure in $O(n \lg (N/n))$ time and $O(n + z \lg (N/z))$ space. Our algorithms are deterministic and always return correct answers.

Connected through hyperedge HE-1 (induced by source paper "compressed indexing with signature grammars"):
  with members:
    - "fast prefix search in little space with applications"
    - "time space trade offs for lempel ziv compressed indexing"
    - "optimal time text indexing in bwt runs bounded space"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "optimal dynamic strings"
    - "orthogonal range searching on the ram revisited"
    - "faster fully compressed pattern matching by recompression"

Connected through hyperedge HE-2 (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"
    - "in place longest common extensions"
    - "deterministic sub linear space lce data structures with efficient construction"
    - "longest common extensions in sublinear space"
    - "computing runs on a general alphabet"

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)