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: "string synchronizing sets sublinear time bwt construction and optimal lce data structure"
  Abstract: Burrows-Wheeler transform (BWT) is an invertible text transformation that, given a text $T$ of length $n$, permutes its symbols according to the lexicographic order of suffixes of $T$. BWT is one of the most heavily studied algorithms in data compression with numerous applications in indexing, sequence analysis, and bioinformatics. Its construction is a bottleneck in many scenarios, and settling the complexity of this task is one of the most important unsolved problems in sequence analysis that has remained open for 25 years. Given a binary string of length $n$, occupying $O(n/\log n)$ machine words, the BWT construction algorithm due to Hon et al. (SIAM J. Comput., 2009) runs in $O(n)$ time and $O(n/\log n)$ space. Recent advancements (Belazzougui, STOC 2014, and Munro et al., SODA 2017) focus on removing the alphabet-size dependency in the time complexity, but they still require $\Omega(n)$ time. #R##N#In this paper, we propose the first algorithm that breaks the $O(n)$-time barrier for BWT construction. Given a binary string of length $n$, our procedure builds the Burrows-Wheeler transform in $O(n/\sqrt{\log n})$ time and $O(n/\log n)$ space. We complement this result with a conditional lower bound proving that any further progress in the time complexity of BWT construction would yield faster algorithms for the very well studied problem of counting inversions: it would improve the state-of-the-art $O(m\sqrt{\log m})$-time solution by Chan and Pǎtrascu (SODA 2010). Our algorithm is based on a novel concept of string synchronizing sets, which is of independent interest. As one of the applications, we show that this technique lets us design a data structure of the optimal size $O(n/\log n)$ that answers Longest Common Extension queries (LCE queries) in $O(1)$ time and, furthermore, can be deterministically constructed in the optimal $O(n/\log n)$ time.

Connected through hyperedge HE-1 (induced by source paper "approximating text to pattern hamming distances"):
  with members:
    - "l p pattern matching in a stream"
    - "the streaming k mismatch problem"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "approximate hamming distance in a stream"
    - "the k mismatch problem revisited"

Connected through hyperedge HE-2 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering"):
  with members:
    - "on the hardness and inapproximability of recognizing wheeler graphs"
    - "resolution of the burrows wheeler transform conjecture"
    - "tunneling on wheeler graphs"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "optimal construction of compressed indexes for highly repetitive texts"
    - "at the roots of dictionary compression string attractors"
    - "optimal time text indexing in bwt runs bounded space"
    - "space efficient construction of compressed indexes in deterministic linear time"

Connected through hyperedge HE-3 (induced by source paper "locally consistent parsing for text indexing in small space"):
  with members:
    - "sparse suffix tree construction in optimal time and space"
    - "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-4 (induced by source paper "linear time string indexing and analysis in small space"):
  with members:
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "a framework for space efficient string kernels"
    - "linear time construction of compressed text indices in compact space"
    - "linear time computation of minimal absent words using suffix array"
    - "optimal dynamic sequence representations"
    - "combined data structure for previous and next smaller values"
    - "more haste less waste lowering the redundancy in fully indexable dictionaries"
    - "optimal succinctness for range minimum queries"

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)