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: "adversarially learned anomaly detection"
  Abstract: Anomaly detection is a significant and hence well-studied problem. However, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge. As Generative Adversarial Networks (GANs) are able to model the complex high-dimensional distributions of real-world data, they offer a promising approach to address this challenge. In this work, we propose an anomaly detection method, Adversarially Learned Anomaly Detection (ALAD) based on bi-directional GANs, that derives adversarially learned features for the anomaly detection task. ALAD then uses reconstruction errors based on these adversarially learned features to determine if a data sample is anomalous. ALAD builds on recent advances to ensure data-space and latent-space cycle-consistencies and stabilize GAN training, which results in significantly improved anomaly detection performance. ALAD achieves state-of-the-art performance on a range of image and tabular datasets while being several hundred-fold faster at test time than the only published GAN-based method.

Connected through hyperedge HE-1 (induced by source paper "memory augmented generative adversarial networks for anomaly detection"):
  with members:
    - "regularized cycle consistent generative adversarial network for anomaly detection"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "anomaly detection with multiple hypotheses predictions"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-2 (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"
    - "learning representations of ultrahigh dimensional data for random distance based outlier detection"
    - "one class adversarial nets for fraud 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-3 (induced by source paper "rate distortion optimization guided autoencoder for isometric embedding in euclidean latent space"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "recent advances in autoencoder based representation learning"
    - "variational autoencoders pursue pca directions by accident"
    - "fixing a broken elbo"
    - "the riemannian geometry of deep generative models"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "regularized cycle consistent generative adversarial network for anomaly detection"):
  with members:
    - "anomaly detection with multiple hypotheses predictions"
    - "deep anomaly detection using geometric transformations"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "adversarial feature learning"
    - "deep structured energy based models for anomaly detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "correlation aware deep generative model for unsupervised anomaly detection"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "deep structured energy based models for anomaly detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-6 (induced by source paper "amad adversarial multiscale anomaly detection on high dimensional and time evolving categorical data"):
  with members:
    - "neural related work summarization with a joint context driven attention mechanism"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"
    - "edge2vec representation learning using edge semantics for biomedical knowledge discovery"
    - "generative adversarial networks an overview"
    - "novelty detection with gan"
    - "anomaly detection using one class neural networks"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"

Connected through hyperedge HE-7 (induced by source paper "unsupervised learning of anomaly detection from contaminated image data using simultaneous encoder training"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "real world anomaly detection in surveillance videos"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep anomaly detection using geometric transformations"
    - "generative adversarial networks an overview"

Connected through hyperedge HE-8 (induced by source paper "chester a web delivered locally computed chest x ray disease prediction system"):
  with members:
    - "efficient gan based anomaly detection"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "improved techniques for training gans"
    - "densely connected convolutional networks"
    - "adversarial feature learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

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)