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 "waveletae a wavelet enhanced autoencoder for wind turbine blade icing detection"):
  Abstract: Wind power, as an alternative to burning fossil fuels, is abundant and inexhaustible. To fully utilize wind power, wind farms are usually located in areas of high altitude and facing serious ice conditions, which can lead to serious consequences. Quick detection of blade ice accretion is crucial for the maintenance of wind farms. Unlike traditional methods of installing expensive physical detectors on wind blades, data-driven approaches are increasingly popular for inspecting the wind turbine failures. In this work, we propose a wavelet enhanced autoencoder model (WaveletAE) to identify wind turbine dysfunction by analyzing the multivariate time series monitored by the SCADA system. WaveletAE is enhanced with wavelet detail coefficients to enforce the autoencoder to capture information from multiple scales, and the CNN-LSTM architecture is applied to learn channel-wise and temporal-wise relations. The empirical study shows that the proposed model outperforms other state-of-the-art time series anomaly detection methods for real-world blade icing detection.

Includes members:
  Member "self adversarial variational autoencoder with gaussian anomaly prior distribution for anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anomaly detection in particulate matter sensor using hypothesis pruning generative adversarial network")
  Member "are generative deep models for novelty detection truly better":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "out of distribution detection with distance guarantee in deep generative models")
  Member "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks")
  Member "multilevel wavelet decomposition network for interpretable time series analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for time series classification a review")
  Member "time series classification from scratch with deep neural networks a strong baseline":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting")
  Member "lstm based encoder decoder for multi sensor anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structural deep clustering network")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)