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: "deep learning for time series classification a review"
  Abstract: Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.

Connected through hyperedge HE-1 (induced by source paper "identity recognition in intelligent cars with behavioral data and lstm resnet classifier"):
  with members:
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "text based lstm networks for automatic music composition"
    - "deepid3 face recognition with very deep neural networks"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "collaborative inference for efficient remote monitoring"):
  with members:
    - "efficient inference on deep neural networks by dynamic representations and decision gates"
    - "deep feature compression for collaborative object detection"
    - "doubly nested network for resource efficient inference"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "xgboost a scalable tree boosting system"
    - "doubly convolutional neural networks"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "kryptooracle a real time cryptocurrency price prediction platform using twitter sentiments"):
  with members:
    - "predicting the effects of news sentiments on the stock market"
    - "xgboost a scalable tree boosting system"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-4 (induced by source paper "unsupervised scalable representation learning for multivariate time series"):
  with members:
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "the uea multivariate time series classification archive 2018"
    - "random warping series a random features method for time series embedding"
    - "stochastic video generation with a learned prior"
    - "an efficient framework for learning sentence representations"
    - "som vae interpretable discrete representation learning on time series"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-5 (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"
    - "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"
    - "transfer learning for time series classification"
    - "data augmentation using synthetic data for time series classification with deep residual networks"

Connected through hyperedge HE-6 (induced by source paper "self attention for raw optical satellite time series classification"):
  with members:
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "massively parallel hyperparameter tuning"
    - "the uea multivariate time series classification archive 2018"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tune a research platform for distributed model selection and training"
    - "duplo a dual view point deep learning architecture for time series classification"
    - "the ucr time series archive"
    - "multi temporal land cover classification with sequential recurrent encoders"

Connected through hyperedge HE-7 (induced by source paper "improving robustness of time series classifier with neural ode guided gradient based data augmentation"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "adversarial attacks on deep neural networks for time series classification"
    - "insights into lstm fully convolutional networks for time series classification"
    - "multivariate lstm fcns for time series classification"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "data augmentation using synthetic data for time series classification with deep residual networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "object detection with deep learning a review"

Connected through hyperedge HE-8 (induced by source paper "convtimenet a pre trained deep convolutional neural network for time series classification"):
  with members:
    - "transfer learning for clinical time series analysis using recurrent neural networks"
    - "transfer learning for time series classification"
    - "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"

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)