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: "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.

Connected through hyperedge HE-1 (induced by source paper "translating between wavelet tree and wavelet matrix construction"):
  with members:
    - "simple fast and lightweight parallel wavelet tree construction"

Connected through hyperedge HE-2 (induced by source paper "string synchronizing sets sublinear time bwt construction and optimal lce data structure"):
  with members:
    - "quasi linear time algorithm for longest common circular factor"
    - "locally consistent parsing for text indexing in small space"
    - "text indexing and searching in sublinear time"
    - "optimal construction of compressed indexes for highly repetitive texts"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "linear time construction of compressed text indices in compact space"
    - "internal pattern matching queries in a text and applications"
    - "recompression a simple and powerful technique for word equations"

Connected through hyperedge HE-3 (induced by source paper "internal dictionary matching"):
  with members:
    - "computing all distinct squares in linear time for integer alphabets"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "internal pattern matching queries in a text and applications"
    - "recompression a simple and powerful technique for word equations"
    - "a linear time algorithm for seeds computation"
    - "faster fully compressed pattern matching by recompression"

Connected through hyperedge HE-4 (induced by source paper "text indexing and searching in sublinear time"):
  with members:
    - "orthogonal point location and rectangle stabbing queries in 3 d"
    - "fast compressed self indexes with deterministic linear time construction"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "sparse suffix tree construction in optimal time and space"
    - "deterministic indexing for packed strings"
    - "range predecessor and lempel ziv parsing"
    - "suffix trays and suffix trists structures for faster text indexing"
    - "alphabet dependent string searching with wexponential search trees"

Connected through hyperedge HE-5 (induced by source paper "improved parallel construction of wavelet trees and rank select structures"):
  with members:
    - "parallel construction of wavelet trees on multicore architectures"
    - "simple fast and lightweight parallel wavelet tree construction"
    - "parallel wavelet tree construction"

Connected through hyperedge HE-6 (induced by source paper "parallel wavelet tree construction"):
  with members:
    - "parallelism in randomized incremental algorithms"
    - "parallel local graph clustering"
    - "sorting with asymmetric read and write costs"
    - "efficient algorithms with asymmetric read and write costs"
    - "counting triangles in massive graphs with mapreduce"
    - "parallel graph decompositions using random shifts"
    - "skew strikes back new developments in the theory of join algorithms"
    - "parallel triangle counting in massive streaming graphs"

Connected through hyperedge HE-7 (induced by source paper "range predecessor and lempel ziv parsing"):
  with members:
    - "faster lightweight lempel ziv parsing"
    - "linear time construction of compressed text indices in compact space"
    - "queries on lz bounded encodings"
    - "lightweight lempel ziv parsing"
    - "faster compact on line lempel ziv factorization"
    - "linear time lempel ziv factorization simple fast small"
    - "succinct indices for range queries with applications to orthogonal range maxima"
    - "sorted range reporting"

Connected through hyperedge HE-8 (induced by source paper "internal pattern matching queries in a text and applications"):
  with members:
    - "sorted range reporting revisited"
    - "orthogonal range searching for text indexing"
    - "searching of gapped repeats and subrepetitions in a word"
    - "sorted range reporting"
    - "pattern matching in lempel ziv compressed strings fast simple and deterministic"
    - "on the maximal sum of exponents of runs in a string"

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)