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: "mining closed strict episodes"
  Abstract: Discovering patterns in a sequence is an important aspect of data mining. One popular choice of such patterns are episodes, patterns in sequential data describing events that often occur in the vicinity of each other. Episodes also enforce in which order events are allowed to occur. In this work we introduce a technique for discovering closed episodes. Adopting existing approaches for discovering traditional patterns, such as closed item sets, to episodes is not straightforward. First of all, we cannot define a unique closure based on frequency because an episode may have several closed super episodes. Moreover, to define a closedness concept for episodes we need a subset relationship between episodes, which is not trivial to define. We approach these problems by introducing strict episodes. We argue that this class is general enough, and at the same time we are able to define a natural subset relationship within it and use it efficiently. In order to mine closed episodes we define an auxiliary closure operator. We show that this closure satisfies the needed Galois connection so that we can use the existing framework for mining closed patterns. Discovering the true closed episodes can be done as a post-processing step. We combine these observations into an efficient mining algorithm and demonstrate empirically its performance in practice.

Connected through hyperedge HE-1 (induced by source paper "free rider episode screening via dual partition model"):
  with members:
    - "skopus mining top k sequential patterns under leverage"
    - "keeping it short and simple summarising complex event sequences with multivariate patterns"
    - "ranking episodes using a partition model"
    - "discovering compressing serial episodes from event sequences"
    - "discovering episodes with compact minimal windows"
    - "the long and the short of it summarising event sequences with serial episodes"
    - "summarizing data succinctly with the most informative itemsets"
    - "mining closed episodes with simultaneous events"

Connected through hyperedge HE-2 (induced by source paper "proceedings of the 2017 siam international conference on data mining"):
  with members:
    - "keeping it short and simple summarising complex event sequences with multivariate patterns"
    - "ranking episodes using a partition model"
    - "mining closed episodes with simultaneous events"
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-3 (induced by source paper "ranking episodes using a partition model"):
  with members:
    - "discovering episodes with compact minimal windows"
    - "the long and the short of it summarising event sequences with serial episodes"
    - "mining closed episodes with simultaneous events"

Connected through hyperedge HE-4 (induced by source paper "using answer set programming for pattern mining"):
  with members:
    - "a constraint programming approach for mining sequential patterns in a sequence database"
    - "mining closed episodes with simultaneous events"
    - "a unified view of automata based algorithms for frequent episode discovery"

Connected through hyperedge HE-5 (induced by source paper "discovering episodes with compact minimal windows"):
  with members:
    - "the long and the short of it summarising event sequences with serial episodes"
    - "mining closed episodes with simultaneous events"
    - "significance of episodes based on minimal windows"

Connected through hyperedge HE-6 (induced by source paper "the long and the short of it summarising event sequences with serial episodes"):
  with members:
    - "mining closed episodes with simultaneous events"
    - "significance of episodes based on minimal windows"
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-7 (induced by source paper "mining closed episodes with simultaneous events"):
  with members:
    - "significance of episodes based on minimal windows"

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.DB(Databases)