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: "lempel ziv factorization may be harder than computing all runs"
  Abstract: The complexity of computing the Lempel-Ziv factorization and the set of all runs (= maximal repetitions) is studied in the decision tree model of computation over ordered alphabet. It is known that both these problems can be solved by RAM algorithms in $O(n\log\sigma)$ time, where $n$ is the length of the input string and $\sigma$ is the number of distinct letters in it. We prove an $\Omega(n\log\sigma)$ lower bound on the number of comparisons required to construct the Lempel-Ziv factorization and thereby conclude that a popular technique of computation of runs using the Lempel-Ziv factorization cannot achieve an $o(n\log\sigma)$ time bound. In contrast with this, we exhibit an $O(n)$ decision tree algorithm finding all runs in a string. Therefore, in the decision tree model the runs problem is easier than the Lempel-Ziv factorization. Thus we support the conjecture that there is a linear RAM algorithm finding all runs.

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"
    - "online detection of repetitions with backtracking"

Connected through hyperedge HE-2 (induced by source paper "algorithms to compute the lyndon array"):
  with members:
    - "the runs theorem"

Connected through hyperedge HE-3 (induced by source paper "optimal searching of gapped repeats in a word"):
  with members:
    - "the runs theorem"
    - "longest gapped repeats and palindromes"
    - "beyond the runs theorem"
    - "searching of gapped repeats and subrepetitions in a word"
    - "on primary and secondary repetitions in words"
    - "on the maximal sum of exponents of runs in a string"
    - "on maximal repetitions of arbitrary exponent"

Connected through hyperedge HE-4 (induced by source paper "faster lightweight lempel ziv parsing"):
  with members:
    - "lempel ziv computation in small space lz ciss"
    - "lightweight lempel ziv parsing"
    - "faster compact on line lempel ziv factorization"
    - "succinct dictionary matching with no slowdown"
    - "fully functional static and dynamic succinct trees"

Connected through hyperedge HE-5 (induced by source paper "longest gapped repeats and palindromes"):
  with members:
    - "optimal searching of gapped repeats in a word"
    - "searching of gapped repeats and subrepetitions in a word"
    - "pattern matching in lempel ziv compressed strings fast simple and deterministic"
    - "a minimal periods algorithm with applications"

Connected through hyperedge HE-6 (induced by source paper "computing runs on a general alphabet"):
  with members:
    - "the runs theorem"
    - "faster lightweight lempel ziv parsing"
    - "online detection of repetitions with backtracking"

Connected through hyperedge HE-7 (induced by source paper "finding the leftmost critical factorization on unordered alphabet"):
  with members:
    - "computing runs on a general alphabet"
    - "online detection of repetitions with backtracking"

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)