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: "locally consistent parsing for text indexing in small space"
  Abstract: We consider two closely related problems of text indexing in a sub-linear working space. The first problem is the Sparse Suffix Tree (SST) construction of a set of suffixes $B$ using only $O(|B|)$ words of space. The second problem is the Longest Common Extension (LCE) problem, where for some parameter $1\le\tau\le n$, the goal is to construct a data structure that uses $O(\frac {n}{\tau})$ words of space and can compute the longest common prefix length of any pair of suffixes. We show how to use ideas based on the Locally Consistent Parsing technique, that was introduced by Sahinalp and Vishkin [STOC '94], in some non-trivial ways in order to improve the known results for the above problems. We introduce new Las-Vegas and deterministic algorithms for both problems. #R##N#We introduce the first Las-Vegas SST construction algorithm that takes $O(n)$ time. This is an improvement over the last result of Gawrychowski and Kociumaka [SODA '17] who obtained $O(n)$ time for Monte-Carlo algorithm, and $O(n\sqrt{\log |B|})$ time for Las-Vegas algorithm. In addition, we introduce a randomized Las-Vegas construction for an LCE data structure that can be constructed in linear time and answers queries in $O(\tau)$ time. #R##N#For the deterministic algorithms, we introduce an SST construction algorithm that takes $O(n(\log \frac{n}{|B|}+\log^*n))$ time (for $|B|=\Omega(\log n\log^*n)$). This is the first almost linear time, $O(n\cdot poly\log{n})$, deterministic SST construction algorithm, where all previous algorithms take at least $\Omega\left(\min\{n|B|,\frac{n^2}{|B|}\}\right)$ time. For the LCE problem, we introduce a data structure that answers LCE queries in $O(\tau\sqrt{\log^*n})$ time, with $O(n(\log\tau+\log^*n))$ construction time (for $\tau=O(\frac{n}{\log n\log^*n})$). This data structure improves both query time and construction time upon the results of Tanimura et al. [CPM '16].

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"
    - "longest common substring made fully dynamic"
    - "sparse suffix tree construction in optimal time and space"
    - "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 "string synchronizing sets sublinear time bwt construction and optimal lce data structure"):
  with members:
    - "quasi linear time algorithm for longest common circular factor"
    - "text indexing and searching in sublinear time"
    - "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)