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 "wavelet trees meet suffix trees"):
  Abstract: We present an improved wavelet tree construction algorithm and discuss its applications to a number of rank/select problems for integer keys and strings.#R##N##R##N#Given a string of length n over an alphabet of size σ ≤ n, our method builds the wavelet tree in O(n log σ / [EQUATION]log n) time, improving upon the state-of-the-art algorithm by a factor of [EQUATION]log n. As a consequence, given an array of n integers we can construct in O(n[EQUATION]log n) time a data structure consisting of O(n) machine words and capable of answering rank/select queries for the subranges of the array in O(log n/ log log n) time. This is a log log n-factor improvement in query time compared to Chan and Patrascu (SODA 2010) and a [EQUATION]log n-factor improvement in construction time compared to Brodal et al. (Theor. Comput. Sci. 2011).#R##N##R##N#Next, we switch to stringological context and propose a novel notion of wavelet suffix trees. For a string w of length n, this data structure occupies O(n) words, takes O(n[EQUATION]log n) time to construct, and simultaneously captures the combinatorial structure of substrings of w while enabling efficient top-down traversal and binary search. In particular, with a wavelet suffix tree we are able to answer in O(log|x|) time the following two natural analogues of rank/select queries for suffixes of substrings:#R##N##R##N#1) For substrings x and y of w (given by their endpoints) count the number of suffixes of x that are lexicographically smaller than y;#R##N##R##N#2) For a substring x of w (given by its endpoints) and an integer k, find the k-th lexicographically smallest suffix of x.#R##N##R##N#We further show that wavelet suffix trees allow to compute a run-length-encoded Burrows-Wheeler transform of a substring x of w (again, given by its endpoints) in O(s log |x|) time, where s denotes the length of the resulting run-length encoding. This answers a question by Cormode and Muthukrishnan (SODA 2005), who considered an analogous problem for Lempel-Ziv compression.#R##N##R##N#All our algorithms, except for the construction of wavelet suffix trees, which additionally requires O(n) time in expectation, are deterministic and operate in the word RAM model.

Includes members:
  Member "weighted ancestors in suffix trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithms for anti powers in strings")
  Member "sorted range reporting revisited":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "internal pattern matching queries in a text and applications")
  Member "internal pattern matching queries in a text and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "string synchronizing sets sublinear time bwt construction and optimal lce data structure")
  Member "the wavelet trie maintaining an indexed sequence of strings in compressed space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the case for learned index structures")

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)