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: "robust anomaly detection in images using adversarial autoencoders"
  Abstract: Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis. Autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalies, where the reconstruction error exceeds some threshold. Here we analyze a fundamental problem of this approach when the training set is contaminated with a small fraction of outliers. We find that continued training of autoencoders inevitably reduces the reconstruction error of outliers, and hence degrades the anomaly detection performance. In order to counteract this effect, an adversarial autoencoder architecture is adapted, which imposes a prior distribution on the latent representation, typically placing anomalies into low likelihood-regions. Utilizing the likelihood model, potential anomalies can be identified and rejected already during training, which results in an anomaly detector that is significantly more robust to the presence of outliers during training.

Connected through hyperedge HE-1 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification"):
  with members:
    - "deep semi supervised anomaly detection"
    - "efficient gan based anomaly detection"
    - "deep anomaly detection using geometric transformations"
    - "escaping from collapsing modes in a constrained space"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "began boundary equilibrium generative adversarial networks"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"

Connected through hyperedge HE-2 (induced by source paper "exploring the memorization generalization continuum in deep learning"):
  with members:
    - "two models of double descent for weak features"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "does learning require memorization a short tale about a long tail"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "an empirical study of example forgetting during deep neural network learning"
    - "improving generalization performance by switching from adam to sgd"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal adversarial learning for detecting unseen falls"):
  with members:
    - "anomaly detection in video sequence with appearance motion correspondence"
    - "adversarial spatio temporal learning for video deblurring"
    - "stan spatio temporal adversarial networks for abnormal event detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "satellite image forgery detection and localization using gan and one class classifier"
    - "adversarially learned one class classifier for novelty detection"
    - "deepfall non invasive fall detection with deep spatio temporal convolutional autoencoders"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"

Connected through hyperedge HE-4 (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"
    - "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"
    - "adversarially learned anomaly detection"
    - "generative adversarial networks an overview"

Connected through hyperedge HE-5 (induced by source paper "fence gan towards better anomaly detection"):
  with members:
    - "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"
    - "doping generative data augmentation for unsupervised anomaly detection with gan"
    - "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"

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)