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: "efficient algorithms for the longest common subsequence in k length substrings"
  Abstract: Finding the longest common subsequence in $k$-length substrings (LCS$k$) is a recently proposed problem motivated by computational biology. This is a generalization of the well-known LCS problem in which matching symbols from two sequences $A$ and $B$ are replaced with matching non-overlapping substrings of length $k$ from $A$ and $B$. We propose several algorithms for LCS$k$, being non-trivial incarnations of the major concepts known from LCS research (dynamic programming, sparse dynamic programming, tabulation). Our algorithms make use of a linear-time and linear-space preprocessing finding the occurrences of all the substrings of length $k$ from one sequence in the other sequence.

Connected through hyperedge HE-1 (induced by source paper "fast and simple algorithms for computing both lcs_ k and lcs_ k"):
  with members:
    - "longest common subsequence in at least k length order isomorphic substrings"
    - "lcsk practical similarity metric for long strings"
    - "faster and more accurate sequence alignment with snap"
    - "plagiarism detection using rouge and wordnet"

Connected through hyperedge HE-2 (induced by source paper "longest common subsequence in at least k length order isomorphic substrings"):
  with members:
    - "lcsk practical similarity metric for long strings"
    - "longest common subsequence in k length substrings"
    - "order preserving matching"
    - "inducing the lcp array"

Connected through hyperedge HE-3 (induced by source paper "lcsk practical similarity metric for long strings"):
  with members:
    - "longest common subsequence in k length substrings"
    - "plagiarism detection using rouge and wordnet"

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)