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: "document retrieval on repetitive collections"
  Abstract: Document retrieval aims at finding the most important documents where a pattern appears in a collection of strings. Traditional pattern-matching techniques yield brute-force document retrieval solutions, which has motivated the research on tailored indexes that offer near-optimal performance. However, an experimental study establishing which alternatives are actually better than brute force, and which perform best depending on the collection characteristics, has not been carried out. In this paper we address this shortcoming by exploring the relationship between the nature of the underlying collection and the performance of current methods. Via extensive experiments we show that established solutions are often beaten in practice by brute-force alternatives. We also design new methods that offer superior time/space trade-offs, particularly on repetitive collections.

Connected through hyperedge HE-1 (induced by source paper "fast small and simple document listing on repetitive text collections"):
  with members:
    - "fully functional suffix trees and optimal text searching in bwt runs bounded space"
    - "document listing on repetitive collections with guaranteed performance"
    - "optimal time text indexing in bwt runs bounded space"
    - "document retrieval on repetitive string collections"
    - "a really simple approximation of smallest grammar"
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"

Connected through hyperedge HE-2 (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-3 (induced by source paper "document retrieval on repetitive string collections"):
  with members:
    - "universal indexes for highly repetitive document collections"
    - "composite repetition aware data structures"
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"
    - "from theory to practice plug and play with succinct data structures"
    - "faster compact top k document retrieval"
    - "improved grammar based compressed indexes"
    - "new algorithms on wavelet trees and applications to information retrieval"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-4 (induced by source paper "universal indexes for highly repetitive document collections"):
  with members:
    - "composite repetition aware data structures"
    - "simd compression and the intersection of sorted integers"
    - "improved grammar based compressed indexes"
    - "a faster grammar based self index"

Connected through hyperedge HE-5 (induced by source paper "document counting in practice"):
  with members:
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"
    - "from theory to practice plug and play with succinct data structures"
    - "practical entropy compressed rank select dictionary"

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)