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: "anomaly detection with generative adversarial networks for multivariate time series"
  Abstract: Today's Cyber-Physical Systems (CPSs) are large, complex, and affixed with networked sensors and actuators that are targets for cyber-attacks. Conventional detection techniques are unable to deal with the increasingly dynamic and complex nature of the CPSs. On the other hand, the networked sensors and actuators generate large amounts of data streams that can be continuously monitored for intrusion events. Unsupervised machine learning techniques can be used to model the system behaviour and classify deviant behaviours as possible attacks. In this work, we proposed a novel Generative Adversarial Networks-based Anomaly Detection (GAN-AD) method for such complex networked CPSs. We used LSTM-RNN in our GAN to capture the distribution of the multivariate time series of the sensors and actuators under normal working conditions of a CPS. Instead of treating each sensor's and actuator's time series independently, we model the time series of multiple sensors and actuators in the CPS concurrently to take into account of potential latent interactions between them. To exploit both the generator and the discriminator of our GAN, we deployed the GAN-trained discriminator together with the residuals between generator-reconstructed data and the actual samples to detect possible anomalies in the complex CPS. We used our GAN-AD to distinguish abnormal attacked situations from normal working conditions for a complex six-stage Secure Water Treatment (SWaT) system. Experimental results showed that the proposed strategy is effective in identifying anomalies caused by various attacks with high detection rate and low false positive rate as compared to existing methods.

Connected through hyperedge HE-1 (induced by source paper "likelihood regret an out of distribution detection score for variational auto encoder"):
  with members:
    - "exact information bottleneck with invertible neural networks getting the best of discriminative and generative modeling"
    - "time series anomaly detection with variational autoencoders"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "input complexity and out of distribution detection with likelihood based generative models"
    - "unsupervised out of distribution detection with batch normalization"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "deep anomaly detection with outlier exposure"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-2 (induced by source paper "unsupervised out of distribution detection with batch normalization"):
  with members:
    - "neural processes"
    - "are generative deep models for novelty detection truly better"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "group anomaly detection using deep generative models"
    - "deep anomaly detection with outlier exposure"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "novelty detection with gan"

Connected through hyperedge HE-3 (induced by source paper "probabilistic forecasting of sensory data with generative adversarial networks forgan"):
  with members:
    - "phaselink a deep learning approach to seismic phase association"
    - "predicting inpatient discharge prioritization with electronic health records"
    - "deepdownscale a deep learning strategy for high resolution weather forecast"
    - "voice impersonation using generative adversarial networks"
    - "cred a deep residual network of convolutional and recurrent units for earthquake signal detection"
    - "svsgan singing voice separation via generative adversarial network"
    - "adversarial learning for neural dialogue generation"
    - "model free renewable scenario generation using generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial networks for distributed intrusion detection in the internet of things"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "efficient gan based anomaly detection"
    - "md gan multi discriminator generative adversarial networks for distributed datasets"
    - "mdgan boosting anomaly detection using multi discriminator generative adversarial networks"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "abnormal event detection in videos using generative adversarial nets"

Connected through hyperedge HE-5 (induced by source paper "out of distribution detection using neural rendering generative models"):
  with members:
    - "hybrid models with deep and invertible features"
    - "efficient gan based anomaly detection"
    - "deep anomaly detection with outlier exposure"
    - "neural rendering model joint generation and prediction for semi supervised learning"
    - "on calibration of modern neural networks"
    - "striving for simplicity the all convolutional net"
    - "deep learning face attributes in the wild"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "a novel gan based fault diagnosis approach for imbalanced industrial time series"):
  with members:
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "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-7 (induced by source paper "clustering with outlier removal"):
  with members:
    - "consensus clustering an embedding perspective extension and beyond"
    - "efficient gan based anomaly detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "sparse subspace clustering algorithm theory and applications"
    - "a new local distance based outlier detection approach for scattered real world data"

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)