human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "rocket exceptionally fast and accurate time series classification using random convolutional kernels"):
  Abstract: Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets. Additionally, many existing methods focus on a single type of feature such as shape or frequency. Building on the recent success of convolutional neural networks for time series classification, we show that simple linear classifiers using random convolutional kernels achieve state-of-the-art accuracy with a fraction of the computational expense of existing methods.

Includes members:
  Member "unsupervised scalable representation learning for multivariate time series":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning for few shot time series classification")
  Member "inceptiontime finding alexnet for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking 1d cnn for time series classification a stronger baseline")
  Member "deep neural network ensembles for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "residual attention net for superior cross domain time sequence modeling")
  Member "time series classification for varying length series":
    also connected through other hyperedges:
      (none)
  Member "ts chief a scalable and accurate forest algorithm for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "residual attention net for superior cross domain time sequence modeling")
  Member "the ucr time series archive":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable hybrid hmm with gaussian process emission for sequential time series data clustering")
  Member "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blank language models")
  Member "fast and accurate time series classification with weasel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inceptiontime finding alexnet for time series classification")

Question: Please tell me which class the source paper 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)