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: "online detection of repetitions with backtracking"
  Abstract: In this paper we present two algorithms for the following problem: given a string and a rational $e > 1$, detect in the online fashion the earliest occurrence of a repetition of exponent $\ge e$ in the string. #R##N#1. The first algorithm supports the backtrack operation removing the last letter of the input string. This solution runs in $O(n\log m)$ time and $O(m)$ space, where $m$ is the maximal length of a string generated during the execution of a given sequence of $n$ read and backtrack operations. #R##N#2. The second algorithm works in $O(n\log\sigma)$ time and $O(n)$ space, where $n$ is the length of the input string and $\sigma$ is the number of distinct letters. This algorithm is relatively simple and requires much less memory than the previously known solution with the same working time and space. a string generated during the execution of a given sequence of $n$ read and backtrack operations.

Connected through hyperedge HE-1 (induced by source paper "faster longest common extension queries in strings over general alphabets"):
  with members:
    - "the runs theorem"
    - "longest common extensions in sublinear space"
    - "computing runs on a general alphabet"
    - "finding the leftmost critical factorization on unordered alphabet"
    - "lempel ziv factorization may be harder than computing all runs"

Connected through hyperedge HE-2 (induced by source paper "detecting one variable patterns"):
  with members:
    - "eertree an efficient data structure for processing palindromes in strings"
    - "weighted ancestors in suffix trees"
    - "internal pattern matching queries in a text and applications"
    - "a minimal periods algorithm with applications"

Connected through hyperedge HE-3 (induced by source paper "detecting unary patterns"):
  with members:
    - "the runs theorem"
    - "internal pattern matching queries in a text and applications"
    - "a minimal periods algorithm with applications"

Connected through hyperedge HE-4 (induced by source paper "computing runs on a general alphabet"):
  with members:
    - "the runs theorem"
    - "faster lightweight lempel ziv parsing"
    - "lempel ziv factorization may be harder than computing all runs"

Connected through hyperedge HE-5 (induced by source paper "finding the leftmost critical factorization on unordered alphabet"):
  with members:
    - "computing runs on a general alphabet"
    - "lempel ziv factorization may be harder than computing all runs"

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)