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: "one class adversarial nets for fraud detection"
  Abstract: Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users. However, in practice, there are often no or very few records of malicious users. In this paper, we develop one-class adversarial nets (OCAN) for fraud detection using training data with only benign users. OCAN first uses LSTM-Autoencoder to learn the representations of benign users from their sequences of online activities. It then detects malicious users by training a discriminator with a complementary GAN model that is different from the regular GAN model. Experimental results show that our OCAN outperforms the state-of-the-art one-class classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.

Connected through hyperedge HE-1 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection"):
  with members:
    - "deep semi supervised anomaly detection"
    - "one class convolutional neural network"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "latent space autoregression for novelty detection"
    - "spectral normalization for generative adversarial networks"

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"
    - "are generative deep models for novelty detection truly better"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "deep weakly supervised anomaly detection"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "deep semi supervised anomaly detection"
    - "efficient training for positive unlabeled learning"
    - "adversarially learned anomaly detection"
    - "learning representations of ultrahigh dimensional data for random distance based outlier detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "prototypical networks for few shot learning"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-4 (induced by source paper "using generative adversarial networks to synthesize artificial financial datasets"):
  with members:
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "on convergence and stability of gans"
    - "ecommercegan a generative adversarial network for e commerce"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "improved training of wasserstein gans"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-5 (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"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "no place to hide catching fraudulent entities in tensors"
    - "anomaly detection using one class neural networks"
    - "unmasking the abnormal events in video"

Connected through hyperedge HE-6 (induced by source paper "gan augmented text anomaly detection with sequences of deep statistics"):
  with members:
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "texygen a benchmarking platform for text generation models"
    - "anomaly detection via minimum likelihood generative adversarial networks"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "good semi supervised learning that requires a bad gan"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "adversarial ranking for language generation"
    - "long text generation via adversarial training with leaked information"

Connected through hyperedge HE-7 (induced by source paper "improving the security of image manipulation detection through one and a half class multiple classification"):
  with members:
    - "satellite image forgery detection and localization using gan and one class classifier"
    - "cnn based detection of generic constrast adjustment with jpeg post processing"
    - "autoencoder with recurrent neural networks for video forgery detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "learning temporal regularity in video sequences"
    - "the limitations of deep learning in adversarial settings"
    - "evasion attacks against machine learning at test time"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-8 (induced by source paper "detection of accounting anomalies in the latent space using adversarial autoencoder neural networks"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "triple generative adversarial nets"
    - "solving the false positives problem in fraud prediction"
    - "detection of anomalies in large scale accounting data using deep autoencoder networks"
    - "empirical evaluation of rectified activations in convolutional network"
    - "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)