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: "sparse suffix tree construction in optimal time and space"
  Abstract: Suffix tree (and the closely related suffix array) are fundamental structures capturing all substrings of a given text essentially by storing all its suffixes in the lexicographical order. In some applications, we work with a subset of $b$ interesting suffixes, which are stored in the so-called sparse suffix tree. Because the size of this structure is $\Theta(b)$, it is natural to seek a construction algorithm using only $O(b)$ words of space assuming read-only random access to the text. We design a linear-time Monte Carlo algorithm for this problem, hence resolving an open question explicitly stated by Bille et al. [TALG 2016]. The best previously known algorithm by I et al. [STACS 2014] works in $O(n\log b)$ time. Our solution proceeds in $n/b$ rounds; in the $r$-th round, we consider all suffixes starting at positions congruent to $r$ modulo $n/b$. By maintaining rolling hashes, we lexicographically sort all interesting suffixes starting at such positions, and then we merge them with the already considered suffixes. For efficient merging, we also need to answer LCE queries in small space. By plugging in the structure of Bille et al. [CPM 2015] we obtain $O(n+b\log b)$ time complexity. We improve this structure, which implies a linear-time sparse suffix tree construction algorithm. We complement our Monte Carlo algorithm with a deterministic verification procedure. The verification takes $O(n\sqrt{\log b})$ time, which improves upon the bound of $O(n\log b)$ obtained by I et al. [STACS 2014]. This is obtained by first observing that the pruning done inside the previous solution has a rather clean description using the notion of graph spanners with small multiplicative stretch. Then, we are able to decrease the verification time by applying difference covers twice. Combined with the Monte Carlo algorithm, this gives us an $O(n\sqrt{\log b})$-time and $O(b)$-space Las Vegas algorithm.

Connected through hyperedge HE-1 (induced by source paper "time space tradeoffs for finding a long common substring"):
  with members:
    - "faster queries for longest substring palindrome after block edit"
    - "locally consistent parsing for text indexing in small space"
    - "longest common substring made fully dynamic"
    - "longest common substrings with k mismatches"
    - "approximating lz77 via small space multiple pattern matching"
    - "sublinear space algorithms for the longest common substring problem"

Connected through hyperedge HE-2 (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-3 (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"
    - "fast label extraction in the cdawg"
    - "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-4 (induced by source paper "optimal time dictionary compressed indexes"):
  with members:
    - "optimal rank and select queries on dictionary compressed text"
    - "fast prefix search in little space with applications"
    - "universal compressed text indexing"
    - "representing the suffix tree with the cdawg"
    - "time space trade offs for lempel ziv compressed indexing"
    - "at the roots of dictionary compression string attractors"
    - "compressed indexing with signature grammars"
    - "document listing on repetitive collections with guaranteed performance"

Connected through hyperedge HE-5 (induced by source paper "locally consistent parsing for text indexing in small space"):
  with members:
    - "string synchronizing sets sublinear time bwt construction and optimal lce data structure"
    - "tight lower bounds for the longest common extension problem"
    - "longest common extensions in sublinear space"
    - "internal pattern matching queries in a text and applications"

Connected through hyperedge HE-6 (induced by source paper "text indexing and searching in sublinear time"):
  with members:
    - "orthogonal point location and rectangle stabbing queries in 3 d"
    - "fast compressed self indexes with deterministic linear time construction"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "deterministic indexing for packed strings"
    - "range predecessor and lempel ziv parsing"
    - "wavelet trees meet suffix trees"
    - "suffix trays and suffix trists structures for faster text indexing"
    - "alphabet dependent string searching with wexponential search trees"

Connected through hyperedge HE-7 (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"
    - "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)