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: "self adversarial variational autoencoder with gaussian anomaly prior distribution for anomaly detection"
  Abstract: Recently, deep generative models have become increasingly popular in unsupervised anomaly detection. However, deep generative models aim at recovering the data distribution rather than detecting anomalies. Besides, deep generative models have the risk of overfitting training samples, which has disastrous effects on anomaly detection performance. To solve the above two problems, we propose a Self-adversarial Variational Autoencoder with a Gaussian anomaly prior assumption. We assume that both the anomalous and the normal prior distribution are Gaussian and have overlaps in the latent space. Therefore, a Gaussian transformer net T is trained to synthesize anomalous but near-normal latent variables. Keeping the original training objective of Variational Autoencoder, besides, the generator G tries to distinguish between the normal latent variables and the anomalous ones synthesized by T, and the encoder E is trained to discriminate whether the output of G is real. These new objectives we added not only give both G and E the ability to discriminate but also introduce additional regularization to prevent overfitting. Compared with the SOTA baselines, the proposed model achieves significant improvements in extensive experiments. Datasets and our model are available at a Github repository.

Connected through hyperedge HE-1 (induced by source paper "anomaly detection in particulate matter sensor using hypothesis pruning generative adversarial network"):
  with members:
    - "ablation studies in artificial neural networks"
    - "time series anomaly detection with variational autoencoders"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "anomaly detection with multiple hypotheses predictions"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "adversarial feature learning"
    - "structured pruning of deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "waveletae a wavelet enhanced autoencoder for wind turbine blade icing detection"):
  with members:
    - "are generative deep models for novelty detection truly better"
    - "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-3 (induced by source paper "augmenting monte carlo dropout classification models with unsupervised learning tasks for detecting and diagnosing out of distribution faults"):
  with members:
    - "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection"
    - "detecting and diagnosing incipient building faults using uncertainty information from deep neural networks"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "a one class support vector machine calibration method for time series change point detection"

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)