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: "doping generative data augmentation for unsupervised anomaly detection with gan"
  Abstract: Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets. Previous work primarily focused on data augmentation for semi-supervised and supervised tasks. In this paper, we instead focus on unsupervised anomaly detection and propose a novel generative data augmentation framework optimized for this task. In particular, we propose to oversample infrequent normal samples - normal samples that occur with small probability, e.g., rare normal events. We show that these samples are responsible for false positives in anomaly detection. However, oversampling of infrequent normal samples is challenging for real-world high-dimensional data with multimodal distributions. To address this challenge, we propose to use a GAN variant known as the adversarial autoencoder (AAE) to transform the high-dimensional multimodal data distributions into low-dimensional unimodal latent distributions with well-defined tail probability. Then, we systematically oversample at the `edge' of the latent distributions to increase the density of infrequent normal samples. We show that our oversampling pipeline is a unified one: it is generally applicable to datasets with different complex data distributions. To the best of our knowledge, our method is the first data augmentation technique focused on improving performance in unsupervised anomaly detection. We validate our method by demonstrating consistent improvements across several real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "rcc dual gan an efficient approach for outlier detection with few identified anomalies"):
  with members:
    - "heartbeat anomaly detection using adversarial oversampling"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"
    - "anomaly detection via minimum likelihood generative adversarial networks"
    - "generative adversarial active learning for unsupervised outlier detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"

Connected through hyperedge HE-2 (induced by source paper "self supervised gan analysis and improvement with multi class minimax game"):
  with members:
    - "an improved self supervised gan via adversarial training"
    - "self supervised gans via auxiliary rotation loss"
    - "improving gan with neighbors embedding and gradient matching"
    - "varying k lipschitz constraint for generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "rob gan generator discriminator and adversarial attacker"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-3 (induced by source paper "a closer look at feature space data augmentation for few shot intent classification"):
  with members:
    - "augmenting data with mixup for sentence classification an empirical study"
    - "data augmentation with atomic templates for spoken language understanding"
    - "data augmentation for low resource sentiment analysis using generative adversarial networks"
    - "few shot learning with meta metric learners"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "active learning for new domains in natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "this dataset does not exist training models from generated images"):
  with members:
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "classification accuracy score for conditional generative models"
    - "on the steerability of generative adversarial networks"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "interpreting the latent space of gans for semantic face editing"
    - "how good is my gan"
    - "spectral normalization for generative adversarial networks"
    - "bagan data augmentation with balancing gan"

Connected through hyperedge HE-5 (induced by source paper "exploring bias in gan based data augmentation for small samples"):
  with members:
    - "gansfer learning combining labelled and unlabelled data for gan based data augmentation"
    - "imagining an engineer on gan based data augmentation perpetuating biases"
    - "t cgan conditional generative adversarial network for data augmentation in noisy time series with irregular sampling"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "code switching sentence generation by generative adversarial networks and its application to data augmentation"
    - "pixel level data augmentation for semantic image segmentation using generative adversarial networks"
    - "prostategan mitigating data bias via prostate diffusion imaging synthesis with generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "fence gan towards better anomaly detection"):
  with members:
    - "robust anomaly detection in images using adversarial autoencoders"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "good semi supervised learning that requires a bad gan"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"
    - "deep structured energy based models for anomaly detection"

Connected through hyperedge HE-7 (induced by source paper "autoencoders and generative adversarial networks for imbalanced sequence classification"):
  with members:
    - "improved training of wasserstein gans"
    - "detection of anomalies in large scale accounting data using deep autoencoder networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-8 (induced by source paper "a novel gan based fault diagnosis approach for imbalanced industrial time series"):
  with members:
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "improved techniques for training gans"
    - "adversarial feature learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "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)