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: "text indexing and searching in sublinear time"
  Abstract: We introduce the first index that can be built in $o(n)$ time for a text of length $n$, and also queried in $o(m)$ time for a pattern of length $m$. On a constant-size alphabet, for example, our index uses $O(n\log^{1/2+\varepsilon}n)$ bits, is built in $O(n/\log^{1/2-\varepsilon} n)$ deterministic time, and finds the $\mathrm{occ}$ pattern occurrences in time $O(m/\log n + \sqrt{\log n}\log\log n + \mathrm{occ})$, where $\varepsilon>0$ is an arbitrarily small constant. As a comparison, the most recent classical text index uses $O(n\log n)$ bits, is built in $O(n)$ time, and searches in time $O(m/\log n + \log\log n + \mathrm{occ})$. We build on a novel text sampling based on difference covers, which enjoys properties that allow us efficiently computing longest common prefixes in constant time. We extend our results to the secondary memory model as well, where we give the first construction in $o(Sort(n))$ time of a data structure with suffix array functionality, which can search for patterns in the almost optimal time, with an additive penalty of $O(\sqrt{\log_{M/B} n}\log\log n)$, where $M$ is the size of main memory available and $B$ is the disk block size.

Connected through hyperedge HE-1 (induced by source paper "string synchronizing sets sublinear time bwt construction and optimal lce data structure"):
  with members:
    - "quasi linear time algorithm for longest common circular factor"
    - "locally consistent parsing for text indexing in small space"
    - "optimal construction of compressed indexes for highly repetitive texts"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "wavelet trees meet suffix trees"
    - "linear time construction of compressed text indices in compact space"
    - "internal pattern matching queries in a text and applications"
    - "recompression a simple and powerful technique for word equations"

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)