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: "new algorithms for regular expression matching"
  Abstract: In this paper we revisit the classical regular expression matching problem, namely, given a regular expression $R$ and a string $Q$, decide if $Q$ matches one of the strings specified by $R$. Let $m$ and $n$ be the length of $R$ and $Q$, respectively. On a standard unit-cost RAM with word length $w \geq \log n$, we show that the problem can be solved in $O(m)$ space with the following running times: \begin{equation*} \begin{cases} #R##N#O(n\frac{m \log w}{w} + m \log w) & \text{if $m > w$} \\ #R##N#O(n\log m + m\log m) & \text{if $\sqrt{w} < m \leq w$} \\ #R##N#O(\min(n+ m^2, n\log m + m\log m)) & \text{if $m \leq \sqrt{w}$.} \end{cases} \end{equation*} This improves the best known time bound among algorithms using $O(m)$ space. Whenever $w \geq \log^2 n$ it improves all known time bounds regardless of how much space is used.

Connected through hyperedge HE-1 (induced by source paper "sparse regular expression matching"):
  with members:
    - "which regular expression patterns are hard to match"
    - "fast and compact regular expression matching"

Connected through hyperedge HE-2 (induced by source paper "from regular expression matching to parsing"):
  with members:
    - "which regular expression patterns are hard to match"
    - "fast and compact regular expression matching"

Connected through hyperedge HE-3 (induced by source paper "pattern matching in trees and strings"):
  with members:
    - "an optimal decomposition algorithm for tree edit distance"
    - "fast and compact regular expression matching"

Connected through hyperedge HE-4 (induced by source paper "improved approximate string matching and regular expression matching on ziv lempel compressed texts"):
  with members:
    - "fast and compact regular expression matching"

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)