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: "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
  Abstract: Nowadays, multivariate time series data are increasingly collected in various real world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in multivariate time series refer to identifying abnormal status in certain time steps and pinpointing the root causes. Building such a system, however, is challenging since it not only requires to capture the temporal dependency in each time series, but also need encode the inter-correlations between different pairs of time series. In addition, the system should be robust to noise and provide operators with different levels of anomaly scores based upon the severity of different incidents. Despite the fact that a number of unsupervised anomaly detection algorithms have been developed, few of them can jointly address these challenges. In this paper, we propose a Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED), to perform anomaly detection and diagnosis in multivariate time series data. Specifically, MSCRED first constructs multi-scale (resolution) signature matrices to characterize multiple levels of the system statuses in different time steps. Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns. Finally, based upon the feature maps which encode the inter-sensor correlations and temporal information, a convolutional decoder is used to reconstruct the input signature matrices and the residual signature matrices are further utilized to detect and diagnose anomalies. Extensive empirical studies based on a synthetic dataset and a real power plant dataset demonstrate that MSCRED can outperform state-of-the-art baseline methods.

Connected through hyperedge HE-1 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks"):
  with members:
    - "robustperiod time frequency mining for robust multiple periodicities detection"
    - "time series anomaly detection service at microsoft"
    - "deep semantic segmentation of natural and medical images a review"
    - "time series anomaly detection using convolutional neural networks and transfer learning"
    - "deeply supervised salient object detection with short connections"
    - "robusttrend a huber loss with a combined first and second order difference regularization for time series trend filtering"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "robuststl a robust seasonal trend decomposition algorithm for long time series"

Connected through hyperedge HE-2 (induced by source paper "oiad one for all image anomaly detection with disentanglement learning"):
  with members:
    - "bayes factor vae hierarchical bayesian deep auto encoder models for factor disentanglement"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a neural algorithm of artistic style"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "autoencoding beyond pixels using a learned similarity metric"

Connected through hyperedge HE-3 (induced by source paper "cratos cogination of reliable algorithm for time series optimal solution"):
  with members:
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"

Connected through hyperedge HE-4 (induced by source paper "fraud detection in networks state of the art"):
  with members:
    - "unsupervised dictionary learning for anomaly detection"
    - "spotting collective behaviour of online frauds in customer reviews"
    - "deep learning for anomaly detection a survey"
    - "midas microcluster based detector of anomalies in edge streams"
    - "no place to hide catching fraudulent entities in tensors"
    - "anomaly detection using one class neural networks"
    - "one class adversarial nets for fraud detection"
    - "unmasking the abnormal events in video"

Connected through hyperedge HE-5 (induced by source paper "waveletfcnn a deep time series classification model for wind turbine blade icing detection"):
  with members:
    - "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection"
    - "are generative deep models for novelty detection truly better"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "wavelet convolutional neural networks"
    - "fast and accurate time series classification with weasel"
    - "deep convolutional neural network for inverse problems in imaging"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-6 (induced by source paper "visual analytics of anomalous user behaviors a survey"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "the spread of low credibility content by social bots"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "graph based anomaly detection and description a survey"
    - "a fraud detection visualization system utilizing radial drawings and heat maps"
    - "occupational fraud detection through visualization"

Connected through hyperedge HE-7 (induced by source paper "rsm gan a convolutional recurrent gan for anomaly detection in contaminated seasonal multivariate time series"):
  with members:
    - "unsupervised learning of anomaly detection from contaminated image data using simultaneous encoder training"
    - "efficient gan based anomaly detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "improved training of wasserstein gans"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "waveletae a wavelet enhanced autoencoder for wind turbine blade icing detection"):
  with members:
    - "self adversarial variational autoencoder with gaussian anomaly prior distribution for anomaly detection"
    - "are generative deep models for novelty detection truly better"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "adam a method for stochastic optimization"

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)