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: "approximate pattern matching with k mismatches in packed text"
  Abstract: Given strings $P$ of length $m$ and $T$ of length $n$ over an alphabet of size $\sigma$, the string matching with $k$-mismatches problem is to find the positions of all the substrings in $T$ that are at Hamming distance at most $k$ from $P$. If $T$ can be read only one character at the time the best known bounds are $O(n\sqrt{k\log k})$ and $O(n + n\sqrt{k/w}\log k)$ in the word-RAM model with word length $w$. In the RAM models (including $AC^0$ and word-RAM) it is possible to read up to $\floor{w / \log \sigma}$ characters in constant time if the characters of $T$ are encoded using $\ceil{\log \sigma}$ bits. The only solution for $k$-mismatches in packed text works in $O((n \log\sigma/\log n)\ceil{m \log (k + \log n / \log\sigma) / w} + n^{\varepsilon})$ time, for any $\varepsilon > 0$. We present an algorithm that runs in time $O(\frac{n}{\floor{w/(m\log\sigma)}} (1 + \log \min(k,\sigma) \log m / \log\sigma))$ in the $AC^0$ model if $m=O(w / \log\sigma)$ and $T$ is given packed. We also describe a simpler variant that runs in time $O(\frac{n}{\floor{w/(m\log\sigma)}}\log \min(m, \log w / \log\sigma))$ in the word-RAM model. The algorithms improve the existing bound for $w = \Omega(\log^{1+\epsilon}n)$, for any $\epsilon > 0$. Based on the introduced technique, we present algorithms for several other approximate matching problems.

Connected through hyperedge HE-1 (induced by source paper "a note on the longest common substring with k mismatches problem"):
  with members:
    - "longest common substrings with k mismatches"

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)