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 "free rider episode screening via dual partition model"):
  Abstract: One of the drawbacks of frequent episode mining is that overwhelmingly many of the discovered patterns are redundant. Free-rider episode, as a typical example, consists of a real pattern doped with some additional noise events. Because of the possible high support of the inside noise events, such free-rider episodes may have abnormally high support that they cannot be filtered by frequency based framework. An effective technique for filtering free-rider episodes is using a partition model to divide an episode into two consecutive subepisodes and comparing the observed support of such episode with its expected support under the assumption that these two subepisodes occur independently. In this paper, we take more complex subepisodes into consideration and develop a novel partition model named EDP for free-rider episode filtering from a given set of episodes. It combines (1) a dual partition strategy which divides an episode to an underlying real pattern and potential noises; (2) a novel definition of the expected support of a free-rider episode based on the proposed partition strategy. We can deem the episode interesting if the observed support is substantially higher than the expected support estimated by our model. The experiments on synthetic and real-world datasets demonstrate EDP can effectively filter free-rider episodes compared with existing state-of-the-arts.

Includes members:
  Member "skopus mining top k sequential patterns under leverage":
    also connected through other hyperedges:
      (none)
  Member "keeping it short and simple summarising complex event sequences with multivariate patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interpretable and steerable sequence learning via prototypes")
  Member "ranking episodes using a partition model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "proceedings of the 2017 siam international conference on data mining")
  Member "discovering compressing serial episodes from event sequences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "summarizing event sequences with serial episodes a statistical model and an application")
  Member "discovering episodes with compact minimal windows":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "skopus mining top k sequential patterns under leverage")
  Member "the long and the short of it summarising event sequences with serial episodes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "mining insights from weakly structured event data")
  Member "mining closed episodes with simultaneous events":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discovering high utility episodes in sequences")

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.DB(Databases)