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 episodes with simultaneous events"
  Abstract: Sequential pattern discovery is a well-studied field in data mining. Episodes are sequential patterns describing events that often occur in the vicinity of each other. Episodes can impose restrictions to the order of the events, which makes them a versatile technique for describing complex patterns in the sequence. Most of the research on episodes deals with special cases such as serial, parallel, and injective episodes, while discovering general episodes is understudied. In this paper we extend the definition of an episode in order to be able to represent cases where events often occur simultaneously. We present an efficient and novel miner for discovering frequent and closed general episodes. Such a task presents unique challenges. Firstly, we cannot define closure based on frequency. We solve this by computing a more conservative closure that we use to reduce the search space and discover the closed episodes as a postprocessing step. Secondly, episodes are traditionally presented as directed acyclic graphs. We argue that this representation has drawbacks leading to redundancy in the output. We solve these drawbacks by defining a subset relationship in such a way that allows us to remove the redundant episodes. We demonstrate the efficiency of our algorithm and the need for using closed episodes empirically on synthetic and real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "discovering high utility episodes in sequences"):
  with members:
    - "proum projection based utility mining on sequence data"
    - "fast utility mining on complex sequences"
    - "a survey of utility oriented pattern mining"
    - "privacy preserving utility mining a survey"

Connected through hyperedge HE-2 (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 strict episodes"

Connected through hyperedge HE-3 (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 strict episodes"
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-4 (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 strict episodes"

Connected through hyperedge HE-5 (induced by source paper "keeping it short and simple summarising complex event sequences with multivariate patterns"):
  with members:
    - "the long and the short of it summarising event sequences with serial episodes"
    - "significance of episodes based on minimal windows"

Connected through hyperedge HE-6 (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 strict episodes"
    - "a unified view of automata based algorithms for frequent episode discovery"

Connected through hyperedge HE-7 (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 strict episodes"
    - "significance of episodes based on minimal windows"

Connected through hyperedge HE-8 (induced by source paper "the long and the short of it summarising event sequences with serial episodes"):
  with members:
    - "mining closed strict episodes"
    - "significance of episodes based on minimal windows"
    - "kolmogorov s structure functions and model selection"

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)