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 index for weighted sequences"
  Abstract: The problem of finding factors of a text string which are identical or similar to a given pattern string is a central problem in computer science. A generalised version of this problem consists in implementing an index over the text to support efficient on-line pattern queries. We study this problem in the case where the text is weighted: for every position of the text and every letter of the alphabet a probability of occurrence of this letter at this position is given. Sequences of this type, also called position weight matrices, are commonly used to represent imprecise or uncertain data. A weighted sequence may represent many different strings, each with probability of occurrence equal to the product of probabilities of its letters at subsequent positions. Given a probability threshold $1/z$, we say that a pattern string $P$ matches a weighted text at position $i$ if the product of probabilities of the letters of $P$ at positions $i,\ldots,i+|P|-1$ in the text is at least $1/z$. In this article, we present an $O(nz)$-time construction of an $O(nz)$-sized index that can answer pattern matching queries in a weighted text in optimal time improving upon the state of the art by a factor of $z \log z$. Other applications of this data structure include an $O(nz)$-time construction of the weighted prefix table and an $O(nz)$-time computation of all covers of a weighted sequence, which improve upon the state of the art by the same factor.

Connected through hyperedge HE-1 (induced by source paper "experimental evaluation of algorithms for computing quasiperiods"):
  with members:
    - "indexing weighted sequences neat and efficient"
    - "two strings at hamming distance 1 cannot be both quasiperiodic"
    - "computing covers using prefix tables"
    - "covering problems for partial words and for indeterminate strings"
    - "a linear time algorithm for seeds computation"

Connected through hyperedge HE-2 (induced by source paper "weighted shortest common supersequence problem revisited"):
  with members:
    - "longest common subsequence on weighted sequences"
    - "indexing weighted sequences neat and efficient"
    - "faster space efficient algorithms for subset sum k sum and related problems"
    - "pattern matching and consensus problems on weighted sequences and profiles"
    - "streaming k mismatch with error correcting and applications"

Connected through hyperedge HE-3 (induced by source paper "indexing weighted sequences neat and efficient"):
  with members:
    - "pattern matching and consensus problems on weighted sequences and profiles"
    - "probabilistic threshold indexing for uncertain strings"
    - "fast average case pattern matching on weighted sequences"
    - "weighted ancestors in suffix trees"
    - "a linear time algorithm for seeds computation"

Connected through hyperedge HE-4 (induced by source paper "streaming k mismatch with error correcting and applications"):
  with members:
    - "the streaming k mismatch problem"
    - "indexing weighted sequences neat and efficient"
    - "pattern matching and consensus problems on weighted sequences and profiles"
    - "approximate hamming distance in a stream"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "probabilistic threshold indexing for uncertain strings"
    - "space lower bounds for online pattern matching"

Connected through hyperedge HE-5 (induced by source paper "pattern matching and consensus problems on weighted sequences and profiles"):
  with members:
    - "streaming k mismatch with error correcting and applications"
    - "polynomial kernels for weighted problems"
    - "probabilistic threshold indexing for uncertain strings"
    - "fast average case pattern matching on weighted sequences"

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)