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: "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
  Abstract: Despite inherent ill-definition, anomaly detection is a research endeavor of great interest within machine learning and visual scene understanding alike. Most commonly, anomaly detection is considered as the detection of outliers within a given data distribution based on some measure of normality. The most significant challenge in real-world anomaly detection problems is that available data is highly imbalanced towards normality (i.e. non-anomalous) and contains a most a subset of all possible anomalous samples - hence limiting the use of well-established supervised learning methods. By contrast, we introduce an unsupervised anomaly detection model, trained only on the normal (non-anomalous, plentiful) samples in order to learn the normality distribution of the domain and hence detect abnormality based on deviation from this model. Our proposed approach employs an encoder-decoder convolutional neural network with skip connections to thoroughly capture the multi-scale distribution of the normal data distribution in high-dimensional image space. Furthermore, utilizing an adversarial training scheme for this chosen architecture provides superior reconstruction both within high-dimensional image space and a lower-dimensional latent vector space encoding. Minimizing the reconstruction error metric within both the image and hidden vector spaces during training aids the model to learn the distribution of normality as required. Higher reconstruction metrics during subsequent test and deployment are thus indicative of a deviation from this normal distribution, hence indicative of an anomaly. Experimentation over established anomaly detection benchmarks and challenging real-world datasets, within the context of X-ray security screening, shows the unique promise of such a proposed approach.

Connected through hyperedge HE-1 (induced by source paper "towards automatic threat detection a survey of advances of deep learning within x ray security imaging"):
  with members:
    - "evaluation of a dual convolutional neural network architecture for object wise anomaly detection in cluttered x ray security imagery"
    - "on the impact of object and sub component level segmentation strategies for supervised anomaly detection within x ray security imagery"
    - "toward automatic threat recognition for airport x ray baggage screening with deep convolutional object detection"
    - "analyzing and improving the image quality of stylegan"
    - "deep cmst framework for the autonomous recognition of heavily occluded and cluttered baggage items from multivendor security radiographs"
    - "sixray a large scale security inspection x ray benchmark for prohibited item discovery in overlapping images"
    - "evaluating the transferability and adversarial discrimination of convolutional neural networks for threat object detection and classification within x ray security imagery"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "cascaded structure tensor framework for robust identification of heavily occluded baggage items from multi vendor x ray scans"):
  with members:
    - "deep learning in video multi object tracking a survey"
    - "evaluation of a dual convolutional neural network architecture for object wise anomaly detection in cluttered x ray security imagery"
    - "a survey of deep learning based object detection"
    - "evaluating the transferability and adversarial discrimination of convolutional neural networks for threat object detection and classification within x ray security imagery"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "chest x ray inpainting with deep generative models"
    - "deep affinity network for multiple object tracking"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-3 (induced by source paper "graph embedded pose clustering for anomaly detection"):
  with members:
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "pose flow efficient online pose tracking"
    - "deep clustering for unsupervised learning of visual features"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "latent space autoregression for novelty detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "attention is all you need"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

Connected through hyperedge HE-4 (induced by source paper "inverse transform autoencoder for anomaly detection"):
  with members:
    - "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"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-5 (induced by source paper "evaluation of a dual convolutional neural network architecture for object wise anomaly detection in cluttered x ray security imagery"):
  with members:
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "mask r cnn"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "automated detection of smuggled high risk security threats using deep learning"

Connected through hyperedge HE-6 (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"
    - "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"
    - "adversarially learned anomaly detection"
    - "generative adversarial networks an overview"

Connected through hyperedge HE-7 (induced by source paper "a novel gan based fault diagnosis approach for imbalanced industrial time series"):
  with members:
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "doping generative data augmentation for unsupervised anomaly detection with gan"
    - "improved techniques for training gans"
    - "adversarial feature learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "deep cmst framework for the autonomous recognition of heavily occluded and cluttered baggage items from multivendor security radiographs"):
  with members:
    - "a survey of deep learning based object detection"
    - "chest x ray inpainting with deep generative models"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)