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: "ganomaly semi supervised anomaly detection via adversarial training"
  Abstract: Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While this can be addressed as a supervised learning problem, a significantly more challenging problem is that of detecting the unknown/unseen anomaly case that takes us instead into the space of a one-class, semi-supervised learning paradigm. We introduce such a novel anomaly detection model, by using a conditional generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space. Employing encoder-decoder-encoder sub-networks in the generator network enables the model to map the input image to a lower dimension vector, which is then used to reconstruct the generated output image. The use of the additional encoder network maps this generated image to its latent representation. Minimizing the distance between these images and the latent vectors during training aids in learning the data distribution for the normal samples. As a result, a larger distance metric from this learned data distribution at inference time is indicative of an outlier from that distribution - an anomaly. Experimentation over several benchmark datasets, from varying domains, shows the model efficacy and superiority over previous state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "deep learning for anomaly detection a survey"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "rare event detection using disentangled representation learning"
    - "real world anomaly detection in surveillance videos"
    - "adversarially learned one class classifier for novelty detection"
    - "latent space autoregression for novelty detection"

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"
    - "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"
    - "one class adversarial nets for fraud detection"

Connected through hyperedge HE-3 (induced by source paper "anomaly detection in particulate matter sensor using hypothesis pruning generative adversarial network"):
  with members:
    - "ablation studies in artificial neural networks"
    - "time series anomaly detection with variational autoencoders"
    - "self adversarial variational autoencoder with gaussian anomaly prior distribution for anomaly detection"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "anomaly detection with multiple hypotheses predictions"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "adversarial feature learning"
    - "structured pruning of deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "oiad one for all image anomaly detection with disentanglement learning"):
  with members:
    - "bayes factor vae hierarchical bayesian deep auto encoder models for factor disentanglement"
    - "efficient gan based anomaly detection"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a neural algorithm of artistic style"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "autoencoding beyond pixels using a learned similarity metric"

Connected through hyperedge HE-5 (induced by source paper "anomaly detection by latent regularized dual adversarial networks"):
  with members:
    - "anomaly detection with adversarial dual autoencoders"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "began boundary equilibrium generative adversarial networks"
    - "abnormal event detection in videos using generative adversarial nets"
    - "energy based generative adversarial network"

Connected through hyperedge HE-6 (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"
    - "adversarially learned one class classifier for novelty detection"
    - "anomaly detection via minimum likelihood generative adversarial networks"
    - "generative adversarial active learning for unsupervised outlier detection"
    - "doping generative data augmentation for unsupervised anomaly detection with gan"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"

Connected through hyperedge HE-7 (induced by source paper "mimicgan robust projection onto image manifolds with corruption mimicking"):
  with members:
    - "image2stylegan how to embed images into the stylegan latent space"
    - "efficient gan based anomaly detection"
    - "universal decision based black box perturbations breaking security through obscurity defenses"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "nam non adversarial unsupervised domain mapping"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-8 (induced by source paper "deep semi supervised anomaly detection"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "deep learning for anomaly detection a survey"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"

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.CV(Computer Vision and Pattern Recognition)