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: "keeping it short and simple summarising complex event sequences with multivariate patterns"
  Abstract: We study how to obtain concise descriptions of discrete multivariate sequential data. In particular, how to do so in terms of rich multivariate sequential patterns that can capture potentially highly interesting (cor)relations between sequences. To this end we allow our pattern language to span over the domains (alphabets) of all sequences, allow patterns to overlap temporally, as well as allow for gaps in their occurrences. #R##N#We formalise our goal by the Minimum Description Length principle, by which our objective is to discover the set of patterns that provides the most succinct description of the data. To discover high-quality pattern sets directly from data, we introduce DITTO, a highly efficient algorithm that approximates the ideal result very well. #R##N#Experiments show that DITTO correctly discovers the patterns planted in synthetic data. Moreover, it scales favourably with the length of the data, the number of attributes, the alphabet sizes. On real data, ranging from sensor networks to annotated text, DITTO discovers easily interpretable summaries that provide clear insight in both the univariate and multivariate structure.

Connected through hyperedge HE-1 (induced by source paper "interpretable and steerable sequence learning via prototypes"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "ecg heartbeat classification a deep transferable representation"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "automatic rule extraction from long short term memory networks"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "automatic text scoring using neural networks"

Connected through hyperedge HE-2 (induced by source paper "detecting path anomalies in time series data on networks"):
  with members:
    - "from relational data to graphs inferring significant links using generalized hypergeometric ensembles"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "mixedtrails bayesian hypothesis comparison on heterogeneous sequential data"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "representing higher order dependencies in networks"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-3 (induced by source paper "free rider episode screening via dual partition model"):
  with members:
    - "skopus mining top k sequential patterns under leverage"
    - "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"
    - "mining closed strict episodes"

Connected through hyperedge HE-4 (induced by source paper "proceedings of the 2017 siam international conference on data mining"):
  with members:
    - "ranking episodes using a partition model"
    - "mining closed episodes with simultaneous events"
    - "mining closed strict episodes"
    - "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.AI(Artificial Intelligence)