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: "ultra fast shapelets for time series classification"
  Abstract: Time series shapelets are discriminative subsequences and their similarity to a time series can be used for time series classification. Since the discovery of time series shapelets is costly in terms of time, the applicability on long or multivariate time series is difficult. In this work we propose Ultra-Fast Shapelets that uses a number of random shapelets. It is shown that Ultra-Fast Shapelets yield the same prediction quality as current state-of-the-art shapelet-based time series classifiers that carefully select the shapelets by being by up to three orders of magnitudes. Since this method allows a ultra-fast shapelet discovery, using shapelets for long multivariate time series classification becomes feasible. #R##N#A method for using shapelets for multivariate time series is proposed and Ultra-Fast Shapelets is proven to be successful in comparison to state-of-the-art multivariate time series classifiers on 15 multivariate time series datasets from various domains. Finally, time series derivatives that have proven to be useful for other time series classifiers are investigated for the shapelet-based classifiers. It is shown that they have a positive impact and that they are easy to integrate with a simple preprocessing step, without the need of adapting the shapelet discovery algorithm.

Connected through hyperedge HE-1 (induced by source paper "multivariate lstm fcns for time series classification"):
  with members:
    - "lstm fully convolutional networks for time series classification"
    - "multivariate time series classification with weasel muse"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multivariate time series classification using dynamic time warping template selection for human activity recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "how to construct deep recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "gendis genetic discovery of shapelets"):
  with members:
    - "the heterogeneous ensembles of standard classification algorithms hesca the whole is greater than the sum of its parts"
    - "scalable discovery of time series shapelets"
    - "should we really use post hoc tests based on mean ranks"

Connected through hyperedge HE-3 (induced by source paper "explainable time series tweaking via irreversible and reversible temporal transformations"):
  with members:
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "highly comparative feature based time series classification"
    - "a time series forest for classification and feature extraction"
    - "cost sensitive classification empirical evaluation of a hybrid genetic decision tree induction algorithm"

Connected through hyperedge HE-4 (induced by source paper "multivariate time series classification with weasel muse"):
  with members:
    - "multivariate lstm fcns for time series classification"

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)