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: "transfer learning for time series classification"
  Abstract: Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network’s weights) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network’s generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike for image recognition problems, transfer learning techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved if the model is fine-tuned from a pre-trained neural network instead of training it from scratch. In this paper, we fill this gap by investigating how to transfer deep CNNs for the TSC task. To evaluate the potential of transfer learning, we performed extensive experiments using the UCR archive which is the largest publicly available TSC benchmark containing 85 datasets. For each dataset in the archive, we pre-trained a model and then fine-tuned it on the other datasets resulting in 7140 different deep neural networks. These experiments revealed that transfer learning can improve or degrade the models predictions depending on the dataset used for transfer. Therefore, in an effort to predict the best source dataset for a given target dataset, we propose a new method relying on Dynamic Time Warping to measure inter-datasets similarities. We describe how our method can guide the transfer to choose the best source dataset leading to an improvement in accuracy on 71 out of 85 datasets.

Connected through hyperedge HE-1 (induced by source paper "meta learning framework with applications to zero shot time series forecasting"):
  with members:
    - "energy predictive models with limited data using transfer learning"
    - "decentralized flood forecasting using deep neural networks"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "financial time series forecasting with deep learning a systematic literature review 2005 2019"
    - "n beats neural basis expansion analysis for interpretable time series forecasting"
    - "meta learning with latent embedding optimization"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"

Connected through hyperedge HE-2 (induced by source paper "forecasting industrial aging processes with machine learning methods"):
  with members:
    - "the autofeat python library for automatic feature engineering and selection"
    - "methods for interpreting and understanding deep neural networks"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "pixel recurrent neural networks"
    - "deep learning in neural networks"
    - "advances in optimizing recurrent networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep learning for time series classification a review"):
  with members:
    - "detecting and interpreting myocardial infarction using fully convolutional neural networks"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "deep learning for sensor based activity recognition a survey"
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "cost sensitive convolution based neural networks for imbalanced time series classification"
    - "genesis of basic and multi layer echo state network recurrent autoencoders for efficient data representations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "data augmentation using synthetic data for time series classification with deep residual networks"

Connected through hyperedge HE-4 (induced by source paper "meta learning for few shot time series classification"):
  with members:
    - "convtimenet a pre trained deep convolutional neural network for time series classification"
    - "unsupervised scalable representation learning for multivariate time series"
    - "deep learning for time series classification a review"
    - "a cnn adapted to time series for the classification of supernovae"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "autowarp learning a warping distance from unlabeled time series using sequence autoencoders"
    - "data augmentation using synthetic data for time series classification with deep residual networks"

Connected through hyperedge HE-5 (induced by source paper "convtimenet a pre trained deep convolutional neural network for time series classification"):
  with members:
    - "deep learning for time series classification a review"
    - "transfer learning for clinical time series analysis using recurrent neural networks"
    - "data augmentation using synthetic data for time series classification with deep residual networks"
    - "lstm fully convolutional networks for time series classification"
    - "towards a universal neural network encoder for time series"
    - "multi task self supervised visual learning"
    - "timenet pre trained deep recurrent neural network for time series classification"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-6 (induced by source paper "deep learning on mobile devices a review"):
  with members:
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the limits and potentials of deep learning for robotics"
    - "ai benchmark running deep neural networks on android smartphones"
    - "an analysis of deep neural network models for practical applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "deep learning in robotics a review of recent research"

Connected through hyperedge HE-7 (induced by source paper "inceptiontime finding alexnet for time series classification"):
  with members:
    - "convtimenet a pre trained deep convolutional neural network for time series classification"
    - "automatic alignment of surgical videos using kinematic data"
    - "deep neural network ensembles for time series classification"
    - "adversarial attacks on deep neural networks for time series classification"
    - "a cnn adapted to time series for the classification of supernovae"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "the ucr time series archive"

Connected through hyperedge HE-8 (induced by source paper "deep neural network ensembles for time series classification"):
  with members:
    - "adversarial attacks on deep neural networks for time series classification"
    - "deep learning for time series classification a review"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "deep neural network ensemble by data augmentation and bagging for skin lesion classification"
    - "data augmentation using synthetic data for time series classification with deep residual networks"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "towards a universal neural network encoder for time series"
    - "time series classification from scratch with deep neural networks a strong baseline"

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)