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 "optimal top k document retrieval"):
  Abstract: Let D be a collection of D documents, which are strings over an alphabet of size σ, of total length n. We describe a data structure that uses linear space and and reports k most relevant documents that contain a query pattern P, which is a string of length p, in time O(p/logn+k), which is optimal in the RAM model in the general case where lgD = �(logn), and involves a novel RAM-optimal suffix tree search. Our construction supports an ample set of important relevance measures, such as the number of times P appears in a document (called term frequency), a fixed document importance, and the minimal distance between two occurrences of P in a document. When lgD = o(logn), we show how to reduce the space of the data structure from O(nlogn) to O(n(logσ+logD+loglogn)) bits, and to O(n(logσ+logD)) bits in the case of the popular term frequency measure of relevance, at the price of an additive term O(log " nlogσ) in the query time, for any constant e > 0. We also consider the dynamic scenario, where documents can be inserted and deleted from the collection. We obtain linear space and query time O(p(loglogn) 2 /logn+logn+kloglogk), whereas insertions and deletions require O(log 1+" n) time per symbol, for any constant e > 0. Finally, we consider an extended static scenario where an extra parameter par(P,d) is de- fined, and the query must retrieve only documents d such that par(P,d) ∈ (τ1,τ2), where this range is specified at query time. We solve these queries using linear space and O(p/logn + log 1+" n + k log " n) time, for any constant e > 0. Our technique is to translate these top-k problems into multidimensional geometric search problems. As an additional bonus, we describe some improvements to those problems.

Includes members:
  Member "fast prefix search in little space with applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space")
  Member "alphabet dependent string searching with wexponential search trees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "achieving optimal backlog in the vanilla multi processor cup game")
  Member "spaces trees and colors the algorithmic landscape of document retrieval on sequences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast small and simple document listing on repetitive text collections")
  Member "faster compact top k document retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "document retrieval on repetitive string collections")
  Member "orthogonal range searching on the ram revisited":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "path query data structures in practice")
  Member "towards an optimal space and query time index for top k document retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spaces trees and colors the algorithmic landscape of document retrieval on sequences")
  Member "space efficient data analysis queries on grids":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "document listing on repetitive collections with guaranteed performance")
  Member "new algorithms on wavelet trees and applications to information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "edge minimization in de bruijn graphs")

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)