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: "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.

Connected through hyperedge HE-1 (induced by source paper "fully functional bidirectional burrows wheeler indexes"):
  with members:
    - "simulating the dna string graph in succinct space"
    - "assembling omnitigs using hidden order de bruijn graphs"
    - "representing the suffix tree with the cdawg"
    - "fast label extraction in the cdawg"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "linear time construction of compressed text indices in compact space"
    - "variable order de bruijn graphs"
    - "engineering small space dictionary matching"

Connected through hyperedge HE-2 (induced by source paper "space efficient construction of compressed suffix trees"):
  with members:
    - "space efficient computation of the lcp array from the burrows wheeler transform"
    - "detecting mutations by ebwt"
    - "external memory bwt and lcp computation for sequence collections with applications"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "lightweight lcp construction for very large collections of strings"
    - "linear time construction of compressed text indices in compact space"
    - "optimal dynamic sequence representations"

Connected through hyperedge HE-3 (induced by source paper "fast compressed self indexes with deterministic linear time construction"):
  with members:
    - "fast prefix search in little space with applications"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "deterministic indexing for packed strings"
    - "linear time construction of compressed text indices in compact space"
    - "suffix trays and suffix trists structures for faster text indexing"
    - "alphabet dependent string searching with wexponential search trees"
    - "optimal lower and upper bounds for representing sequences"
    - "compression via matroids a randomized polynomial kernel for odd cycle transversal"

Connected through hyperedge HE-4 (induced by source paper "burrows wheeler transform and lcp array construction in constant space"):
  with members:
    - "low space external memory construction of the succinct permuted longest common prefix array"
    - "faster average case low memory semi external construction of the burrows wheeler transform"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "in place longest common extensions"
    - "lightweight lcp construction for very large collections of strings"
    - "linear time construction of compressed text indices in compact space"
    - "lightweight lcp construction for next generation sequencing datasets"
    - "optimal dynamic sequence representations"

Connected through hyperedge HE-5 (induced by source paper "space efficient construction of compressed indexes in deterministic linear time"):
  with members:
    - "range predecessor and lempel ziv parsing"
    - "dynamic data structures for document collections and graphs"
    - "linear time construction of compressed text indices in compact space"
    - "optimal dynamic sequence representations"
    - "efficient fully compressed sequence representations"
    - "more haste less waste lowering the redundancy in fully indexable dictionaries"
    - "fully functional static and dynamic succinct trees"

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)