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: "deep anomaly detection using geometric transformations"
  Abstract: We consider the problem of anomaly detection in images, and present a new detection technique. Given a sample of images, all known to belong to a "normal" class (e.g., dogs), we show how to train a deep neural model that can detect out-of-distribution images (i.e., non-dog objects). The main idea behind our scheme is to train a multi-class model to discriminate between dozens of geometric transformations applied on all the given images. The auxiliary expertise learned by the model generates feature detectors that effectively identify, at test time, anomalous images based on the softmax activation statistics of the model when applied on transformed images. We present extensive experiments using the proposed detector, which indicate that our algorithm improves state-of-the-art methods by a wide margin.

Connected through hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey"):
  with members:
    - "unmask adversarial detection and defense through robust feature alignment"
    - "a research agenda dynamic models to defend against correlated attacks"
    - "analysis of confident classifiers for out of distribution detection"
    - "adversarial attacks on time series"
    - "adversarial attacks on deep neural networks for time series classification"
    - "mimicgan robust projection onto image manifolds with corruption mimicking"
    - "analyzing adversarial attacks against deep learning for intrusion detection in iot networks"
    - "deep learning for anomaly detection a survey"

Connected through hyperedge HE-2 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection"):
  with members:
    - "deep semi supervised anomaly detection"
    - "one class convolutional neural network"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "adversarially learned one class classifier for novelty detection"
    - "latent space autoregression for novelty detection"
    - "spectral normalization for generative adversarial networks"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-3 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification"):
  with members:
    - "deep semi supervised anomaly detection"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "efficient gan based anomaly detection"
    - "escaping from collapsing modes in a constrained space"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "began boundary equilibrium generative adversarial networks"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"

Connected through hyperedge HE-4 (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"
    - "anomaly detection using one class neural networks"
    - "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-5 (induced by source paper "drocc deep robust one class classification"):
  with members:
    - "deep semi supervised anomaly detection"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "anomaly detection with multiple hypotheses predictions"
    - "deep anomaly detection with outlier exposure"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-6 (induced by source paper "anomaly detection by latent regularized dual adversarial networks"):
  with members:
    - "anomaly detection with adversarial dual autoencoders"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "began boundary equilibrium generative adversarial networks"
    - "abnormal event detection in videos using generative adversarial nets"
    - "energy based generative adversarial network"

Connected through hyperedge HE-7 (induced by source paper "regularized cycle consistent generative adversarial network for anomaly detection"):
  with members:
    - "anomaly detection with multiple hypotheses predictions"
    - "adversarially learned anomaly detection"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "adversarial feature learning"
    - "deep structured energy based models for anomaly detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "unsupervised learning of the set of local maxima"):
  with members:
    - "the mechanics of n player differentiable games"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "understanding deep neural networks with rectified linear units"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning deep representations of appearance and motion for anomalous event detection"

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)