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 "internal pattern matching queries in a text and applications"):
  Abstract: We consider several types of internal queries: questions about subwords of a text. As the main tool we develop an optimal data structure for the problem called here internal pattern matching. This data structure provides constant-time answers to queries about occurrences of one subword $x$ in another subword $y$ of a given text, assuming that $|y|=\mathcal{O}(|x|)$, which allows for a constant-space representation of all occurrences. This problem can be viewed as a natural extension of the well-studied pattern matching problem. The data structure has linear size and admits a linear-time construction algorithm. #R##N#Using the solution to the internal pattern matching problem, we obtain very efficient data structures answering queries about: primitivity of subwords, periods of subwords, general substring compression, and cyclic equivalence of two subwords. All these results improve upon the best previously known counterparts. The linear construction time of our data structure also allows to improve the algorithm for finding $\delta$-subrepetitions in a text (a more general version of maximal repetitions, also called runs). For any fixed $\delta$ we obtain the first linear-time algorithm, which matches the linear time complexity of the algorithm computing runs. Our data structure has already been used as a part of the efficient solutions for subword suffix rank & selection, as well as substring compression using Burrows-Wheeler transform composed with run-length encoding.

Includes members:
  Member "wavelet trees meet suffix trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "translating between wavelet tree and wavelet matrix construction")
  Member "sorted range reporting revisited":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wavelet trees meet suffix trees")
  Member "orthogonal range searching for text indexing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time space trade offs for lempel ziv compressed indexing")
  Member "searching of gapped repeats and subrepetitions in a word":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matching patterns with variables")
  Member "sorted range reporting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "four dimensional dominance range reporting in linear space")
  Member "pattern matching in lempel ziv compressed strings fast simple and deterministic":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal time dictionary compressed indexes")
  Member "on the maximal sum of exponents of runs in a string":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the runs theorem")

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)