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 "rcc dual gan an efficient approach for outlier detection with few identified anomalies"):
  Abstract: Outlier detection is an important task in data mining and many technologies have been explored in various applications. However, due to the default assumption that outliers are non-concentrated, unsupervised outlier detection may not correctly detect group anomalies with higher density levels. As for the supervised outlier detection, although high detection rates and optimal parameters can usually be achieved, obtaining sufficient and correct labels is a time-consuming task. To address these issues, we focus on semi-supervised outlier detection with few identified anomalies, in the hope of using limited labels to achieve high detection accuracy. First, we propose a novel detection model Dual-GAN, which can directly utilize the potential information in identified anomalies to detect discrete outliers and partially identified group anomalies simultaneously. And then, considering the instances with similar output values may not all be similar in a complex data structure, we replace the two MO-GAN components in Dual-GAN with the combination of RCC and M-GAN (RCC-Dual-GAN). In addition, to deal with the evaluation of Nash equilibrium and the selection of optimal model, two evaluation indicators are created and introduced into the two models to make the detection process more intelligent. Extensive experiments on both benchmark datasets and two practical tasks demonstrate that our proposed approaches (i.e., Dual-GAN and RCC-Dual-GAN) can significantly improve the accuracy of outlier detection even with only a few identified anomalies. Moreover, compared with the two MO-GAN components in Dual-GAN, the network structure combining RCC and M-GAN has greater stability in various situations.

Includes members:
  Member "heartbeat anomaly detection using adversarial oversampling":
    also connected through other hyperedges:
      (none)
  Member "efficient gan based anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification")
  Member "ganomaly semi supervised anomaly detection via adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection")
  Member "adversarially learned one class classifier for novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "anomaly detection via minimum likelihood generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gan augmented text anomaly detection with sequences of deep statistics")
  Member "generative adversarial active learning for unsupervised outlier detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection")
  Member "doping generative data augmentation for unsupervised anomaly detection with gan":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised gan analysis and improvement with multi class minimax game")
  Member "unsupervised anomaly detection with generative adversarial networks to guide marker discovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")

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)