human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "skopus mining top k sequential patterns under leverage"):
  Abstract: This paper presents a framework for exact discovery of the top-k sequential patterns under Leverage. It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact discovery of top-k sequential patterns under a given measure of interest. Our interestingness measure employs the partition approach. A pattern is interesting to the extent that it is more frequent than can be explained by assuming independence between any of the pairs of patterns from which it can be composed. The larger the support compared to the expectation under independence, the more interesting is the pattern. We build on these two elements to exactly extract the k sequential patterns with highest leverage, consistent with our definition of expected support. We conduct experiments on both synthetic data with known patterns and real-world datasets; both experiments confirm the consistency and relevance of our approach with regard to the state of the art. This article was published in Data Mining and Knowledge Discovery and is accessible at this http URL

Includes members:
  Member "ranking episodes using a partition model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "free rider episode screening via dual partition model")
  Member "discovering episodes with compact minimal windows":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "free rider episode screening via dual partition model")
  Member "the long and the short of it summarising event sequences with serial episodes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mining insights from weakly structured event data")
  Member "summarizing data succinctly with the most informative itemsets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mining insights from weakly structured event data")
  Member "using background knowledge to rank itemsets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "skopus exact discovery of the most interesting sequential patterns under leverage")
  Member "significance of episodes based on minimal windows":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "summarizing event sequences with serial episodes a statistical model and an application")
  Member "opus an efficient admissible algorithm for unordered search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lormika local rule based model interpretability with k optimal associations")

Question: Please tell me which class the source paper 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.AI(Artificial Intelligence)