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: "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
  Abstract: Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.

Connected through hyperedge HE-1 (induced by source paper "strategy to increase the safety of a dnn based perception for had systems"):
  with members:
    - "self ensembling with gan based data augmentation for domain adaptation in semantic segmentation"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "the ethical dilemma when not setting up cost based decision rules in semantic segmentation"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"
    - "autoaugment learning augmentation policies from data"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-2 (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"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-3 (induced by source paper "detecting the unexpected via image resynthesis"):
  with members:
    - "lightweight probabilistic deep networks"
    - "learning a discriminative feature network for semantic segmentation"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deep object co segmentation"

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"
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "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:
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "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 "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"):
  with members:
    - "efficient gan based anomaly detection"
    - "adversarially learned one class classifier for novelty detection"
    - "generative adversarial networks an overview"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "improved training of wasserstein gans"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "improved techniques for training gans"
    - "adversarial feature learning"

Connected through hyperedge HE-7 (induced by source paper "neural memory plasticity for anomaly detection"):
  with members:
    - "brain tumor type classification via capsule networks"
    - "pedestrian trajectory prediction with structured memory hierarchies"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "learning temporal strategic relationships using generative adversarial imitation learning"
    - "learning dynamic memory networks for object tracking"
    - "applications of deep learning and reinforcement learning to biological data"
    - "tree memory networks for modelling long term temporal dependencies"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

Connected through hyperedge HE-8 (induced by source paper "a promotion method for generation error based video anomaly detection"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder"
    - "adversarially learned one class classifier for novelty detection"
    - "avid adversarial visual irregularity detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "unmasking the abnormal events in video"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

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)