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: "generative probabilistic novelty detection with adversarial autoencoders"
  Abstract: Novelty detection is the problem of identifying whether a new data point is considered to be an inlier or an outlier. We assume that training data is available to describe only the inlier distribution. Recent approaches primarily leverage deep encoder-decoder network architectures to compute a reconstruction error that is used to either compute a novelty score or to train a one-class classifier. While we too leverage a novel network of that kind, we take a probabilistic approach and effectively compute how likely is that a sample was generated by the inlier distribution. We achieve this with two main contributions. First, we make the computation of the novelty probability feasible because we linearize the parameterized manifold capturing the underlying structure of the inlier distribution, and show how the probability factorizes and can be computed with respect to local coordinates of the manifold tangent space. Second, we improved the training of the autoencoder network. An extensive set of results show that the approach achieves state-of-the-art results on several benchmark datasets.

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"
    - "adversarially learned anomaly detection"
    - "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 "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"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "generative adversarial active learning for unsupervised outlier detection"
    - "one class adversarial nets for fraud detection"

Connected through hyperedge HE-3 (induced by source paper "unsupervised pixel level road defect detection via adversarial image to frequency transform"):
  with members:
    - "road crack detection using deep convolutional neural network and adaptive thresholding"
    - "feature pyramid and hierarchical boosting network for pavement crack detection"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "richer convolutional features for edge detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "future frame prediction for anomaly detection a new baseline"
    - "holistically nested edge detection"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "detecting semantic anomalies"):
  with members:
    - "chester a web delivered locally computed chest x ray disease prediction system"
    - "data efficient image recognition with contrastive predictive coding"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "reducing network agnostophobia"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "deep anomaly detection using geometric transformations"

Connected through hyperedge HE-5 (induced by source paper "out of domain detection for natural language understanding in dialog systems"):
  with members:
    - "open world learning and application to product classification"
    - "efficient large scale domain classification with personalized attention"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "learning from positive and unlabeled data a survey"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "joint learning of domain classification and out of domain detection with dynamic class weighting for satisficing false acceptance rates"

Connected through hyperedge HE-6 (induced by source paper "fixing bias in reconstruction based anomaly detection with lipschitz discriminators"):
  with members:
    - "a survey on gans for anomaly detection"
    - "deep double descent where bigger models and more data hurt"
    - "memorization in overparameterized autoencoders"
    - "deep learning for anomaly detection a survey"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"

Connected through hyperedge HE-7 (induced by source paper "detecting out of distribution inputs in deep neural networks using an early layer output"):
  with members:
    - "learning deep features for one class classification"
    - "improving reconstruction autoencoder out of distribution detection with mahalanobis distance"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "on calibration of modern neural networks"
    - "selective classification for deep neural networks"
    - "deep bayesian active learning with image data"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-8 (induced by source paper "this is not what i imagined error detection for semantic segmentation through visual dissimilarity"):
  with members:
    - "bayesian semantic instance segmentation in open set world"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "photographic image synthesis with cascaded refinement networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "perceptual losses for real time style transfer and super resolution"
    - "image to image translation with conditional adversarial networks"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

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)