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 "insights into lstm fully convolutional networks for time series classification"):
  Abstract: Long short-term memory fully convolutional neural networks (LSTM-FCNs) and Attention LSTM-FCN (ALSTM-FCN) have shown to achieve the state-of-the-art performance on the task of classifying time series signals on the old University of California-Riverside (UCR) time series repository. However, there has been no study on why LSTM-FCN and ALSTM-FCN perform well. In this paper, we perform a series of ablation tests (3627 experiments) on the LSTM-FCN and ALSTM-FCN to provide a better understanding of the model and each of its sub-modules. The results from the ablation tests on the ALSTM-FCN and LSTM-FCN show that the LSTM and the FCN blocks perform better when applied in a conjoined manner. Two z-normalizing techniques, z-normalizing each sample independently and z-normalizing the whole dataset, are compared using a Wilcoxson signed-rank test to show a statistical difference in performance. In addition, we provide an understanding of the impact dimension shuffle that has on LSTM-FCN by comparing its performance with LSTM-FCN when no dimension shuffle is applied. Finally, we demonstrate the performance of the LSTM-FCN when the LSTM block is replaced by a gated recurrent unit (GRU), basic neural network (RNN), and dense block.

Includes members:
  Member "deep gated recurrent and convolutional network hybrid model for univariate time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reduced gate convolutional lstm using predictive coding for spatiotemporal prediction")
  Member "multivariate lstm fcns for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "residual attention net for superior cross domain time sequence modeling")
  Member "lstm fully convolutional networks for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convolutional neural networks with intermediate loss for 3d super resolution of ct and mri scans")
  Member "the ucr time series archive":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable hybrid hmm with gaussian process emission for sequential time series data clustering")
  Member "fast and accurate time series classification with weasel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inceptiontime finding alexnet for time series classification")
  Member "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an average compress algorithm for the sample mean problem under dynamic time warping")
  Member "squeeze and excitation networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "time series classification from scratch with deep neural networks a strong baseline":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting")

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)