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 "suffix trays and suffix trists structures for faster text indexing"):
  Abstract: Suffix trees and suffix arrays are two of the most widely used data structures for text indexing. Each uses linear space and can be constructed in linear time for polynomially sized alphabets. However, when it comes to answering queries with worst-case deterministic time bounds, the prior does so in $O(m\log|\Sigma|)$ time, where $m$ is the query size, $|\Sigma|$ is the alphabet size, and the latter does so in $O(m+\log n)$ time, where $n$ is the text size. If one wants to output all appearances of the query, an additive cost of $O(occ)$ time is sufficient, where $occ$ is the size of the output. #R##N#We propose a novel way of combining the two into, what we call, a {\em suffix tray}. The space and construction time remain linear and the query time improves to $O(m+\log|\Sigma|)$ for integer alphabets from a linear range, i.e. $\Sigma \subset \{1,\cdots, cn\}$, for an arbitrary constant $c$. The construction and query are deterministic. Here also an additive $O(occ)$ time is sufficient if one desires to output all appearances of the query. #R##N#We also consider the online version of indexing, where the text arrives online, one character at a time, and indexing queries are answered in tandem. In this variant we create a cross between a suffix tree and a suffix list (a dynamic variant of suffix array) to be called a {\em suffix trist}; it supports queries in $O(m+\log|\Sigma|)$ time. The suffix trist also uses linear space. Furthermore, if there exists an online construction for a linear-space suffix tree such that the cost of adding a character is worst-case deterministic $f(n,|\Sigma|)$ ($n$ is the size of the current text), then one can further update the suffix trist in $O(f(n,|\Sigma|)+\log |\Sigma|)$ time. The best currently known worst-case deterministic bound for $f(n,|\Sigma|)$ is $O(\log n)$ time.

Includes members:
  Member "orthogonal range searching for text indexing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time space trade offs for lempel ziv compressed indexing")
  Member "on line indexing for general alphabets via predecessor queries on subsets of an ordered list":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "achieving optimal backlog in the vanilla multi processor cup game")

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)