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 "augmenting monte carlo dropout classification models with unsupervised learning tasks for detecting and diagnosing out of distribution faults"):
  Abstract: The Monte Carlo dropout method has proved to be a scalable and easy-to-use approach for estimating the uncertainty of deep neural network predictions. This approach was recently applied to Fault Detection and Di-agnosis (FDD) applications to improve the classification performance on incipient faults. In this paper, we propose a novel approach of augmenting the classification model with an additional unsupervised learning task. We justify our choice of algorithm design via an information-theoretical analysis. Our experimental results on three datasets from diverse application domains show that the proposed method leads to improved fault detection and diagnosis performance, especially on out-of-distribution examples including both incipient and unknown faults.

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 "waveletfcnn a deep time series classification model for wind turbine blade icing detection")
  Member "detecting and diagnosing incipient building faults using uncertainty information from deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an encoder decoder based approach for anomaly detection with application in additive manufacturing")
  Member "self adversarial variational autoencoder with gaussian anomaly prior distribution for anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "anomaly detection in particulate matter sensor using hypothesis pruning generative adversarial network")
  Member "mad gan multivariate anomaly detection for time series data with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "a one class support vector machine calibration method for time series change point detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an encoder decoder based approach for anomaly detection with application in additive manufacturing")

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)