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: "fast average case pattern matching on weighted sequences"
  Abstract: A weighted string over an alphabet of size $\sigma$ is a string in which a set of letters may occur at each position with respective occurrence probabilities. Weighted strings, also known as position weight matrices or uncertain sequences, naturally arise in many contexts. In this article, we study the problem of weighted string matching with a special focus on average-case analysis. Given a weighted pattern string $x$ of length $m$, a text string $y$ of length $n>m$, and a cumulative weight threshold $1/z$, defined as the minimal probability of occurrence of factors in a weighted string, we present an algorithm requiring average-case search time $o(n)$ for pattern matching for weight ratio $\frac{z}{m}  m$, and a cumulative weight threshold $1/z$, we present an algorithm requiring average-case search time $o(\sigma n)$ for the same weight ratio. The importance of these results lies on the fact that these algorithms work in average-case sublinear search time in the size of the text, and in linear preprocessing time and space in the size of the pattern, for these ratios.

Connected through hyperedge HE-1 (induced by source paper "indexing weighted sequences neat and efficient"):
  with members:
    - "pattern matching and consensus problems on weighted sequences and profiles"
    - "efficient index for weighted sequences"
    - "probabilistic threshold indexing for uncertain strings"
    - "weighted ancestors in suffix trees"
    - "a linear time algorithm for seeds computation"

Connected through hyperedge HE-2 (induced by source paper "pattern matching and consensus problems on weighted sequences and profiles"):
  with members:
    - "efficient index for weighted sequences"
    - "streaming k mismatch with error correcting and applications"
    - "polynomial kernels for weighted problems"
    - "probabilistic threshold indexing for uncertain strings"

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)