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: "a faster grammar based self index"
  Abstract: To store and search genomic databases efficiently, researchers have recently started building compressed self-indexes based on grammars. In this paper we show how, given a straight-line program with $r$ rules for a string (S [1..n]) whose LZ77 parse consists of $z$ phrases, we can store a self-index for $S$ in $\Oh{r + z \log \log n}$ space such that, given a pattern (P [1..m]), we can list the $\occ$ occurrences of $P$ in $S$ in $\Oh{m^2 + \occ \log \log n}$ time. If the straight-line program is balanced and we accept a small probability of building a faulty index, then we can reduce the $\Oh{m^2}$ term to $\Oh{m \log m}$. All previous self-indexes are larger or slower in the worst case.

Connected through hyperedge HE-1 (induced by source paper "top tree compression of tries"):
  with members:
    - "dynamic packed compact tries revisited"
    - "universal compressed text indexing"
    - "packed compact tries a fast and efficient data structure for online string processing"
    - "linear size cdawg new repetition aware indexing and grammar compression"
    - "time space trade offs for lempel ziv compressed indexing"
    - "compressed indexing with signature grammars"
    - "deterministic indexing for packed strings"
    - "composite repetition aware data structures"

Connected through hyperedge HE-2 (induced by source paper "resolution of the burrows wheeler transform conjecture"):
  with members:
    - "towards a definitive measure of repetitiveness"
    - "optimal rank and select queries on dictionary compressed text"
    - "optimal time dictionary compressed indexes"
    - "fully functional suffix trees and optimal text searching in bwt runs bounded space"
    - "universal compressed text indexing"
    - "time space trade offs for lempel ziv compressed indexing"
    - "optimal construction of compressed indexes for highly repetitive texts"
    - "at the roots of dictionary compression string attractors"

Connected through hyperedge HE-3 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space"):
  with members:
    - "resolution of the burrows wheeler transform conjecture"
    - "faster repetition aware compressed suffix trees based on block trees"
    - "optimal time dictionary compressed indexes"
    - "fast prefix search in little space with applications"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "on the approximation ratio of greedy parsings"
    - "universal compressed text indexing"
    - "prefix free parsing for building big bwts"

Connected through hyperedge HE-4 (induced by source paper "universal compressed text indexing"):
  with members:
    - "fast prefix search in little space with applications"
    - "time space trade offs for lempel ziv compressed indexing"
    - "at the roots of dictionary compression string attractors"
    - "compressed indexing with signature grammars"
    - "fast label extraction in the cdawg"
    - "optimal time text indexing in bwt runs bounded space"
    - "sparse suffix tree construction in optimal time and space"
    - "a self index on block trees"

Connected through hyperedge HE-5 (induced by source paper "on the approximation ratio of greedy parsings"):
  with members:
    - "optimal time dictionary compressed indexes"
    - "at the roots of dictionary compression string attractors"
    - "compression with the tudocomp framework"
    - "optimal time text indexing in bwt runs bounded space"
    - "a separation between run length slps and lz77"
    - "fully dynamic data structure for lce queries in compressed space"
    - "composite repetition aware data structures"
    - "factorizations of the fibonacci infinite word"

Connected through hyperedge HE-6 (induced by source paper "document listing on repetitive collections with guaranteed performance"):
  with members:
    - "fast prefix search in little space with applications"
    - "universal compressed text indexing"
    - "time space trade offs for lempel ziv compressed indexing"
    - "compressed indexing with signature grammars"
    - "optimal time text indexing in bwt runs bounded space"
    - "document retrieval on repetitive string collections"
    - "fully dynamic data structure for lce queries in compressed space"
    - "composite repetition aware data structures"

Connected through hyperedge HE-7 (induced by source paper "optimal time text indexing in bwt runs bounded space"):
  with members:
    - "linear size cdawg new repetition aware indexing and grammar compression"
    - "fast label extraction in the cdawg"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "fully dynamic data structure for lce queries in compressed space"
    - "universal indexes for highly repetitive document collections"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "composite repetition aware data structures"
    - "computing lz77 in run compressed space"

Connected through hyperedge HE-8 (induced by source paper "time space trade offs for lempel ziv compressed indexing"):
  with members:
    - "fast prefix search in little space with applications"
    - "universal indexes for highly repetitive document collections"
    - "composite repetition aware data structures"
    - "searching and indexing genomic databases via kernelization"
    - "queries on lz bounded encodings"
    - "orthogonal range searching for text indexing"
    - "time space trade offs for longest common extensions"
    - "orthogonal range searching on the ram revisited"

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)