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 "deep neural network ensembles for time series classification"):
  Abstract: Deep neural networks have revolutionized many fields such as computer vision and natural language processing. Inspired by this recent success, deep learning started to show promising results for Time Series Classification (TSC). However, neural networks are still behind the state-of-the-art TSC algorithms, that are currently composed of ensembles of 37 non deep learning based classifiers. We attribute this gap in performance due to the lack of neural network ensembles for TSC. Therefore in this paper, we show how an ensemble of 60 deep learning models can significantly improve upon the current state-of-the-art performance of neural networks for TSC, when evaluated over the UCR/UEA archive: the largest publicly available benchmark for time series analysis. Finally, we show how our proposed Neural Network Ensemble (NNE) is the first time series classifier to outperform COTE while reaching similar performance to the current state-of-the-art ensemble HIVE-COTE.

Includes members:
  Member "adversarial attacks on deep neural networks for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "deep learning for time series classification a review":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "identity recognition in intelligent cars with behavioral data and lstm resnet classifier")
  Member "proximity forest an effective and scalable distance based classifier for time series":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for time series classification a review")
  Member "deep neural network ensemble by data augmentation and bagging for skin lesion classification":
    also connected through other hyperedges:
      (none)
  Member "transfer learning for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning framework with applications to zero shot time series forecasting")
  Member "data augmentation using synthetic data for time series classification with deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks")
  Member "evaluating surgical skills from kinematic data using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "action quality assessment using siamese network based deep metric learning")
  Member "towards a universal neural network encoder for time series":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rethinking 1d cnn for time series classification a stronger baseline")

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)