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: "discovering episodes with compact minimal windows"
  Abstract: Discovering the most interesting patterns is the key problem in the field of pattern mining. While ranking or selecting patterns is well-studied for itemsets it is surprisingly under-researched for other, more complex, pattern types. In this paper we propose a new quality measure for episodes. An episode is essentially a set of events with possible restrictions on the order of events. We say that an episode is significant if its occurrence is abnormally compact, that is, only few gap events occur between the actual episode events, when compared to the expected length according to the independence model. We can apply this measure as a post-pruning step by first discovering frequent episodes and then rank them according to this measure. In order to compute the score we will need to compute the mean and the variance according to the independence model. As a main technical contribution we introduce a technique that allows us to compute these values. Such a task is surprisingly complex and in order to solve it we develop intricate finite state machines that allow us to compute the needed statistics. We also show that asymptotically our score can be interpreted as a $$P$$ P value. In our experiments we demonstrate that despite its intricacy our ranking is fast: we can rank tens of thousands episodes in seconds. Our experiments with text data demonstrate that our measure ranks interpretable episodes high.

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"
    - "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"
    - "mining closed strict episodes"

Connected through hyperedge HE-2 (induced by source paper "skopus mining top k sequential patterns under leverage"):
  with members:
    - "ranking episodes using a partition model"
    - "the long and the short of it summarising event sequences with serial episodes"
    - "summarizing data succinctly with the most informative itemsets"
    - "using background knowledge to rank itemsets"
    - "significance of episodes based on minimal windows"
    - "opus an efficient admissible algorithm for unordered search"

Connected through hyperedge HE-3 (induced by source paper "skopus exact discovery of the most interesting sequential patterns under leverage"):
  with members:
    - "ranking episodes using a partition model"
    - "the long and the short of it summarising event sequences with serial episodes"
    - "summarizing data succinctly with the most informative itemsets"
    - "using background knowledge to rank itemsets"

Connected through hyperedge HE-4 (induced by source paper "ranking episodes using a partition model"):
  with members:
    - "the long and the short of it summarising event sequences with serial episodes"
    - "mining closed episodes with simultaneous events"
    - "mining closed strict episodes"

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.LG(Machine Learning)