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: "lstm fully convolutional networks for time series classification"
  Abstract: Fully convolutional neural networks (FCNs) have been shown to achieve the state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification. Our proposed models significantly enhance the performance of fully convolutional networks with a nominal increase in model size and require minimal preprocessing of the data set. The proposed long short term memory fully convolutional network (LSTM-FCN) achieves the state-of-the-art performance compared with others. We also explore the usage of attention mechanism to improve time series classification with the attention long short term memory fully convolutional network (ALSTM-FCN). The attention mechanism allows one to visualize the decision process of the LSTM cell. Furthermore, we propose refinement as a method to enhance the performance of trained models. An overall analysis of the performance of our model is provided and compared with other techniques.

Connected through hyperedge HE-1 (induced by source paper "convolutional neural networks with intermediate loss for 3d super resolution of ct and mri scans"):
  with members:
    - "super resolution of brain mri images using overcomplete dictionaries and nonlocal similarity"
    - "a tensor factorization method for 3 d super resolution with application to dental ct"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "image super resolution using very deep residual channel attention networks"
    - "channel splitting network for single mr image super resolution"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enhanced deep residual networks for single image super resolution"
    - "simultaneous super resolution and cross modality synthesis of 3d medical images using weakly supervised joint convolutional sparse coding"

Connected through hyperedge HE-2 (induced by source paper "residual attention net for superior cross domain time sequence modeling"):
  with members:
    - "deep neural network ensembles for time series classification"
    - "insights into lstm fully convolutional networks for time series classification"
    - "multivariate lstm fcns for time series classification"
    - "ts chief a scalable and accurate forest algorithm for time series classification"
    - "star transformer"
    - "universal transformers"
    - "linked dynamic graph cnn learning on point cloud via linking hierarchical features"
    - "financial series prediction using attention lstm"

Connected through hyperedge HE-3 (induced by source paper "time series forecasting using lstm networks a symbolic approach"):
  with members:
    - "lstm a search space odyssey"
    - "recurrent neural networks for multivariate time series with missing values"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "detecting and interpreting myocardial infarction using fully convolutional neural networks"):
  with members:
    - "deep neural network ensembles for time series classification"
    - "multivariate lstm fcns for time series classification"
    - "tsviz demystification of deep learning models for time series analysis"
    - "ecg heartbeat classification a deep transferable representation"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "axiomatic attribution for deep networks"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

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"
    - "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"
    - "transfer learning for time series classification"

Connected through hyperedge HE-6 (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"
    - "transfer learning for time series classification"
    - "data augmentation using synthetic data for time series classification with deep residual networks"
    - "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-7 (induced by source paper "insights into lstm fully convolutional networks for time series classification"):
  with members:
    - "deep gated recurrent and convolutional network hybrid model for univariate time series classification"
    - "multivariate lstm fcns for time series classification"
    - "the ucr time series archive"
    - "fast and accurate time series classification with weasel"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"
    - "squeeze and excitation networks"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multi scale convolutional neural networks for time series classification"

Connected through hyperedge HE-8 (induced by source paper "explainable failure predictions with rnn classifiers based on time series data"):
  with members:
    - "the challenge of crafting intelligible intelligence"
    - "explainable time series tweaking via irreversible and reversible temporal transformations"
    - "anomaly detection using one class neural networks"
    - "interpretable categorization of heterogeneous time series data"
    - "interpretable explorable approximations of black box models"
    - "attention is all you need"
    - "time dependent representation for neural event sequence prediction"
    - "a glass box interactive machine learning approach for solving np hard problems with the human in the loop"

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)