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 analysis"
  Abstract: In many real-world application, e.g., speech recognition or sleep stage classification, data are captured over the course of time, constituting a Time-Series. Time-Series often contain temporal dependencies that cause two otherwise identical points of time to belong to different classes or predict different behavior. This characteristic generally increases the difficulty of analysing them. Existing techniques often depended on hand-crafted features that were expensive to create and required expert knowledge of the field. With the advent of Deep Learning new models of unsupervised learning of features for Time-series analysis and forecast have been developed. Such new developments are the topic of this paper: a review of the main Deep Learning techniques is presented, and some applications on Time-Series analysis are summaried. The results make it clear that Deep Learning has a lot to contribute to the field.

Connected through hyperedge HE-1 (induced by source paper "rethinking 1d cnn for time series classification a stronger baseline"):
  with members:
    - "convtimenet a pre trained deep convolutional neural network for time series classification"
    - "inceptiontime finding alexnet for time series classification"
    - "towards a universal neural network encoder for time series"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "multi scale convolutional neural networks for time series classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "should we really use post hoc tests based on mean ranks"

Connected through hyperedge HE-2 (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"
    - "transfer learning for time series classification"

Connected through hyperedge HE-3 (induced by source paper "recurrent neural networks for time series forecasting"):
  with members:
    - "lstm a search space odyssey"
    - "multi period time series modeling with sparsity via bayesian variational inference"
    - "forecasting across time series databases using recurrent neural networks on groups of similar series a clustering approach"
    - "dropout feature ranking for deep learning models"
    - "recurrent neural networks for multivariate time series with missing values"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "ar net a simple auto regressive neural network for time series"):
  with members:
    - "autoregressive convolutional recurrent neural network for univariate and multivariate time series prediction"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "attention is all you need"
    - "a unified approach to interpreting model predictions"
    - "multi scale convolutional neural networks for time series classification"
    - "why should i trust you explaining the predictions of any classifier"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-5 (induced by source paper "should i raise the red flag a comprehensive survey of anomaly scoring methods toward mitigating false alarms"):
  with members:
    - "active anomaly detection via ensembles"
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "setting the threshold for high throughput detectors a mathematical approach for ensembles of dynamic heterogeneous probabilistic anomaly detectors"
    - "deep learning for iot big data and streaming analytics a survey"
    - "a meta analysis of the anomaly detection problem"
    - "systematic construction of anomaly detection benchmarks from real data"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"

Connected through hyperedge HE-6 (induced by source paper "dynamic time warp convolutional networks"):
  with members:
    - "deep learning for time series classification a review"
    - "neuralwarp time series similarity with warping networks"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multi scale convolutional neural networks for time series classification"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "an empirical evaluation of similarity measures for time series classification"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-7 (induced by source paper "a non intrusive reduced order modeling framework for quasi geostrophic turbulence"):
  with members:
    - "short note on the behavior of recurrent neural network for noisy dynamical system"
    - "hybrid forecasting of chaotic processes using machine learning in conjunction with a knowledge based model"
    - "dr rnn a deep residual recurrent neural network for model reduction"
    - "efficient construction of local parametric reduced order models using machine learning techniques"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "multivariate time series classification using dilated convolutional neural network"):
  with members:
    - "model free prediction of noisy chaotic time series by deep learning"
    - "recurrent neural networks for multivariate time series with missing values"
    - "adam a method for stochastic optimization"
    - "deeppose human pose estimation via deep neural networks"

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)