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 "waveletfcnn a deep time series classification model for wind turbine blade icing detection"):
  Abstract: Wind power, as an alternative to burning fossil fuels, is plentiful and renewable. Data-driven approaches are increasingly popular for inspecting the wind turbine failures. In this paper, we propose a novel classification-based anomaly detection system for icing detection of the wind turbine blades. #R##N#We effectively combine the deep neural networks and wavelet transformation to identify such failures sequentially across the time. In the training phase, we present a wavelet based fully convolutional neural network (FCNN), namely WaveletFCNN, for the time series classification. We improve the original (FCNN) by augmenting features with the wavelet coefficients. WaveletFCNN outperforms the state-of-the-art FCNN for the univariate time series classification on the UCR time series archive benchmarks. In the detecting phase, we combine the sliding window and majority vote algorithms to provide the timely monitoring of the anomalies. The system has been successfully implemented on a real-world dataset from Goldwind Inc, where the classifier is trained on a multivariate time series dataset and the monitoring algorithm is implemented to capture the abnormal condition on signals from a wind farm.

Includes members:
  Member "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "augmenting monte carlo dropout classification models with unsupervised learning tasks for detecting and diagnosing out of distribution faults")
  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 "learning overparameterized neural networks via stochastic gradient descent on structured data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "multilevel wavelet decomposition network for interpretable time series analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for time series classification a review")
  Member "wavelet convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "translating diffusion wavelets and regularisation into residual networks")
  Member "fast and accurate time series classification with weasel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inceptiontime finding alexnet for time series classification")
  Member "deep convolutional neural network for inverse problems in imaging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")

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)