human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "linear time string indexing and analysis in small space"):
  Abstract: The field of succinct data structures has flourished over the last 16 years. Starting from the compressed suffix array (CSA) by Grossi and Vitter (STOC 2000) and the FM-index by Ferragina and Manzini (FOCS 2000), a number of generalizations and applications of string indexes based on the Burrows-Wheeler transform (BWT) have been developed, all taking an amount of space that is close to the input size in bits. In many large-scale applications, the construction of the index and its usage need to be considered as one unit of computation. Efficient string indexing and analysis in small space lies also at the core of a number of primitives in the data-intensive field of high-throughput DNA sequencing. We report the following advances in string indexing and analysis. We show that the BWT of a string $T\in \{1,\ldots,\sigma\}^n$ can be built in deterministic $O(n)$ time using just $O(n\log{\sigma})$ bits of space, where $\sigma \leq n$. Within the same time and space budget, we can build an index based on the BWT that allows one to enumerate all the internal nodes of the suffix tree of $T$. Many fundamental string analysis problems can be mapped to such enumeration, and can thus be solved in deterministic $O(n)$ time and in $O(n\log{\sigma})$ bits of space from the input string. We also show how to build many of the existing indexes based on the BWT, such as the CSA, the compressed suffix tree (CST), and the bidirectional BWT index, in randomized $O(n)$ time and in $O(n\log{\sigma})$ bits of space. The previously fastest construction algorithms for BWT, CSA and CST, which used $O(n\log{\sigma})$ bits of space, took $O(n\log{\log{\sigma}})$ time for the first two structures, and $O(n\log^{\epsilon}n)$ time for the third, where $\epsilon$ is any positive constant. Contrary to the state of the art, our bidirectional BWT index supports every operation in constant time per element in its output.

Includes members:
  Member "string synchronizing sets sublinear time bwt construction and optimal lce data structure":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating text to pattern hamming distances")
  Member "space efficient construction of compressed indexes in deterministic linear time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering")
  Member "a framework for space efficient string kernels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "minimal absent words in rooted and unrooted trees")
  Member "linear time construction of compressed text indices in compact space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering")
  Member "linear time computation of minimal absent words using suffix array":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "minimal absent words in rooted and unrooted trees")
  Member "optimal dynamic sequence representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "o log log n worst case local decoding and update efficiency for data compression")
  Member "combined data structure for previous and next smaller values":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "space efficient construction of lyndon arrays in linear time")
  Member "more haste less waste lowering the redundancy in fully indexable dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "four dimensional dominance range reporting in linear space")

Question: Please tell me which class the source paper 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)