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: "faster compact top k document retrieval"
  Abstract: An optimal index solving top-k document retrieval [Navarro and Nekrich, SODA12] takes O(m + k) time for a pattern of length m, but its space is at least 80n bytes for a collection of n symbols. We reduce it to 1.5n to 3n bytes, with O(m+(k+log log n) log log n) time, on typical texts. The index is up to 25 times faster than the best previous compressed solutions, and requires at most 5% more space in practice (and in some cases as little as one half). Apart from replacing classical by compressed data structures, our main idea is to replace suffix tree sampling by frequency thresholding to achieve compression.

Connected through hyperedge HE-1 (induced by source paper "document retrieval on repetitive string collections"):
  with members:
    - "universal indexes for highly repetitive document collections"
    - "composite repetition aware data structures"
    - "document retrieval on repetitive collections"
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"
    - "from theory to practice plug and play with succinct data structures"
    - "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-2 (induced by source paper "compact indexes for flexible top k retrieval"):
  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"
    - "quasi succinct indices"
    - "decoding billions of integers per second through vectorization"
    - "new algorithms on wavelet trees and applications to information retrieval"

Connected through hyperedge HE-3 (induced by source paper "document retrieval on repetitive collections"):
  with members:
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"
    - "improved grammar based compressed indexes"

Connected through hyperedge HE-4 (induced by source paper "spaces trees and colors the algorithmic landscape of document retrieval on sequences"):
  with members:
    - "orthogonal range searching for text indexing"
    - "optimal top k document retrieval"
    - "on optimal top k string retrieval"
    - "new lower and upper bounds for representing sequences"
    - "towards an optimal space and query time index for top k document retrieval"
    - "space efficient data analysis queries on grids"
    - "new algorithms on wavelet trees and applications to information retrieval"
    - "optimal trade off for succinct string indexes"

Connected through hyperedge HE-5 (induced by source paper "optimal top k document retrieval"):
  with members:
    - "fast prefix search in little space with applications"
    - "alphabet dependent string searching with wexponential search trees"
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"
    - "orthogonal range searching on the ram revisited"
    - "towards an optimal space and query time index for top k document retrieval"
    - "space efficient data analysis queries on grids"
    - "new algorithms on wavelet trees and applications to information retrieval"
    - "top k color queries for document retrieval"

Connected through hyperedge HE-6 (induced by source paper "from theory to practice plug and play with succinct data structures"):
  with members:
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"
    - "compressed text indexes from theory to practice"
    - "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)