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 using one class neural networks"
  Abstract: We propose a one-class neural network (OC-NN) model to detect anomalies in complex data sets. OC-NN combines the ability of deep networks to extract progressively rich representation of data with the one-class objective of creating a tight envelope around normal data. The OC-NN approach breaks new ground for the following crucial reason: data representation in the hidden layer is driven by the OC-NN objective and is thus customized for anomaly detection. This is a departure from other approaches which use a hybrid approach of learning deep features using an autoencoder and then feeding the features into a separate anomaly detection method like one-class SVM (OC-SVM). The hybrid OC-SVM approach is suboptimal because it is unable to influence representational learning in the hidden layers. A comprehensive set of experiments demonstrate that on complex data sets (like CIFAR and PFAM), OC-NN significantly outperforms existing state-of-the-art anomaly detection methods.

Connected through hyperedge HE-1 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems"):
  with members:
    - "on adaptive attacks to adversarial example defenses"
    - "on evaluating adversarial robustness"
    - "model reuse attacks on deep learning systems"
    - "adversarial patch"
    - "yolov3 an incremental improvement"
    - "lavan localized and visible adversarial noise"
    - "review of deep learning"
    - "hardware trojan attacks on neural networks"

Connected through hyperedge HE-2 (induced by source paper "hierarchical models intrinsic separability in high dimensions"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "deep anomaly detection using geometric transformations"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "deep structured energy based models for anomaly detection"
    - "fitted learning models with awareness of their limits"
    - "towards open set deep networks"

Connected through hyperedge HE-3 (induced by source paper "neural network representation control gaussian isolation machines and cvc regularization"):
  with members:
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "activation functions comparison of trends in practice and research for deep learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "robust deep and inductive anomaly detection"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "a flexible outlier detector based on a topology given by graph communities"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "generative adversarial active learning for unsupervised outlier detection"

Connected through hyperedge HE-5 (induced by source paper "detecting ai trojans using meta neural analysis"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "exploiting unintended feature leakage in collaborative learning"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sentinet detecting physical attacks against deep learning systems"
    - "backdooring convolutional neural networks via targeted weight perturbations"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"

Connected through hyperedge HE-6 (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-7 (induced by source paper "towards visually explaining variational autoencoders"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "sliced wasserstein generative models"
    - "deep learning for anomaly detection a survey"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "re identification with consistent attentive siamese networks"
    - "sharpen focus learning with attention separability and consistency"

Connected through hyperedge HE-8 (induced by source paper "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"):
  with members:
    - "mptv matching pursuit based total variation minimization for image deconvolution"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "and autoregressive novelty detectors"
    - "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.LG(Machine Learning)