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 "timage a robust time series classification pipeline"):
  Abstract: Time series are series of values ordered by time. This kind of data can be found in many real world settings. Classifying time series is a difficult task and an active area of research. This paper investigates the use of transfer learning in Deep Neural Networks and a 2D representation of time series known as Recurrence Plots. In order to utilize the research done in the area of image classification, where Deep Neural Networks have achieved very good results, we use a Residual Neural Networks architecture known as ResNet. As preprocessing of time series is a major part of every time series classification pipeline, the method proposed simplifies this step and requires only few parameters. For the first time we propose a method for multi time series classification: Training a single network to classify all datasets in the archive with one network. We are among the first to evaluate the method on the latest 2018 release of the UCR archive, a well established time series classification benchmarking dataset.

Includes members:
  Member "insights into lstm fully convolutional networks for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "residual attention net for superior cross domain time sequence modeling")
  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 "transfer learning for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta learning framework with applications to zero shot time series forecasting")
  Member "lstm fully convolutional networks for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolutional neural networks with intermediate loss for 3d super resolution of ct and mri scans")
  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 "classification of time series images using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implementation of deep neural networks to classify eeg signals using gramian angular summation field for epilepsy diagnosis")
  Member "imaging time series to improve classification and imputation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "implementation of deep neural networks to classify eeg signals using gramian angular summation field for epilepsy diagnosis")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)