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: "are generative deep models for novelty detection truly better"
  Abstract: Many deep models have been recently proposed for anomaly detection. This paper presents comparison of selected generative deep models and classical anomaly detection methods on an extensive number of non--image benchmark datasets. We provide statistical comparison of the selected models, in many configurations, architectures and hyperparamaters. We arrive to conclusion that performance of the generative models is determined by the process of selection of their hyperparameters. Specifically, performance of the deep generative models deteriorates with decreasing amount of anomalous samples used in hyperparameter selection. In practical scenarios of anomaly detection, none of the deep generative models systematically outperforms the kNN.

Connected through hyperedge HE-1 (induced by source paper "out of distribution detection with distance guarantee in deep generative models"):
  with members:
    - "conditional generative models are not robust"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "group anomaly detection using deep generative models"
    - "deep anomaly detection with outlier exposure"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "variational inference of disentangled latent concepts from unlabeled observations"

Connected through hyperedge HE-2 (induced by source paper "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "pyod a python toolbox for scalable outlier detection"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "generative adversarial active learning for unsupervised outlier detection"
    - "one class adversarial nets for fraud detection"

Connected through hyperedge HE-3 (induced by source paper "unsupervised out of distribution detection with batch normalization"):
  with members:
    - "neural processes"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "group anomaly detection using deep generative models"
    - "deep anomaly detection with outlier exposure"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "novelty detection with gan"

Connected through hyperedge HE-4 (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"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "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-5 (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"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "exploring deep anomaly detection methods based on capsule net"):
  with members:
    - "deep anomaly detection using geometric transformations"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "dynamic routing between capsules"
    - "speech recognition with deep recurrent neural networks"
    - "representation learning a review and new perspectives"

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)