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: "toward supervised anomaly detection"
  Abstract: Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that classical semi-supervised approaches, originating from a supervised classifier, are inappropriate and hardly detect new and unknown anomalies. We argue that semi-supervised anomaly detection needs to ground on the unsupervised learning paradigm and devise a novel algorithm that meets this requirement. Although being intrinsically non-convex, we further show that the optimization problem has a convex equivalent under relatively mild assumptions. Additionally, we propose an active learning strategy to automatically filter candidates for labeling. In an empirical study on network intrusion detection data, we observe that the proposed learning methodology requires much less labeled data than the state-of-the-art, while achieving higher detection accuracies.

Connected through hyperedge HE-1 (induced by source paper "deep weakly supervised anomaly detection"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "deep semi supervised anomaly detection"
    - "efficient training for positive unlabeled learning"
    - "adversarially learned anomaly detection"
    - "learning representations of ultrahigh dimensional data for random distance based outlier detection"
    - "one class adversarial nets for fraud detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "prototypical networks for few shot learning"

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"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "deep anomaly detection using geometric transformations"

Connected through hyperedge HE-3 (induced by source paper "deep variational semi supervised novelty detection"):
  with members:
    - "deep semi supervised anomaly detection"
    - "deep learning for anomaly detection a survey"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "deep anomaly detection using geometric transformations"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "learning sampling distributions for robot motion planning"
    - "safer classification by synthesis"

Connected through hyperedge HE-4 (induced by source paper "anomaly detection for an e commerce pricing system"):
  with members:
    - "pyod a python toolbox for scalable outlier detection"
    - "efficient gan based anomaly detection"
    - "applying deep learning to airbnb search"
    - "xgboost a scalable tree boosting system"
    - "real time anomaly detection for streaming analytics"
    - "deep structured energy based models for anomaly detection"
    - "sequential feature explanations for anomaly detection"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "anomaly detection in images"):
  with members:
    - "efficient gan based anomaly detection"
    - "anomaly detection for skin disease images using variational autoencoder"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "a survey on deep transfer learning"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "active anomaly detection via ensembles insights algorithms and interpretability"):
  with members:
    - "active anomaly detection via ensembles"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "explaining anomalies in groups with characterizing subspace rules"
    - "why should i trust you explaining the predictions of any classifier"
    - "statistical active learning algorithms for noise tolerance and differential privacy"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised deep anomaly detection with pairwise relation learning"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "learning representations of ultrahigh dimensional data for random distance based outlier detection"
    - "prototypical networks for few shot learning"
    - "matching networks for one shot learning"

Connected through hyperedge HE-8 (induced by source paper "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"):
  with members:
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "self normalizing neural networks"
    - "toeplitz inverse covariance based clustering of multivariate time series data"
    - "deep structured energy based models for anomaly detection"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)