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: "small space encoding lce data structure with constant time queries"
  Abstract: The \emph{longest common extension} (\emph{LCE}) problem is to preprocess a given string $w$ of length $n$ so that the length of the longest common prefix between suffixes of $w$ that start at any two given positions is answered quickly. In this paper, we present a data structure of $O(z \tau^2 + \frac{n}{\tau})$ words of space which answers LCE queries in $O(1)$ time and can be built in $O(n \log \sigma)$ time, where $1 \leq \tau \leq \sqrt{n}$ is a parameter, $z$ is the size of the Lempel-Ziv 77 factorization of $w$ and $\sigma$ is the alphabet size. This is an \emph{encoding} data structure, i.e., it does not access the input string $w$ when answering queries and thus $w$ can be deleted after preprocessing. On top of this main result, we obtain further results using (variants of) our LCE data structure, which include the following: #R##N#- For highly repetitive strings where the $z\tau^2$ term is dominated by $\frac{n}{\tau}$, we obtain a \emph{constant-time and sub-linear space} LCE query data structure. #R##N#- Even when the input string is not well compressible via Lempel-Ziv 77 factorization, we still can obtain a \emph{constant-time and sub-linear space} LCE data structure for suitable $\tau$ and for $\sigma \leq 2^{o(\log n)}$. #R##N#- The time-space trade-off lower bounds for the LCE problem by Bille et al. [J. Discrete Algorithms, 25:42-50, 2014] and by Kosolobov [CoRR, abs/1611.02891, 2016] can be "surpassed" in some cases with our LCE data structure.

Connected through hyperedge HE-1 (induced by source paper "a compressed dynamic self index for highly repetitive text collections"):
  with members:
    - "optimal time dictionary compressed indexes"
    - "universal compressed text indexing"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "in place longest common extensions"):
  with members:
    - "burrows wheeler transform and lcp array construction in constant space"
    - "optimal time and space construction of suffix arrays and lcp arrays for integer alphabets"
    - "fast longest common extensions in small space"
    - "optimal in place suffix sorting"
    - "tight lower bounds for the longest common extension problem"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "longest common extensions in sublinear space"
    - "searching of gapped repeats and subrepetitions in a word"

Connected through hyperedge HE-3 (induced by source paper "tight lower bounds for the longest common extension problem"):
  with members:
    - "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"
    - "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"

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)