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 semi supervised anomaly detection"
  Abstract: Deep approaches to anomaly detection have recently shown promising results over shallow approaches on high-dimensional data. Typically anomaly detection is treated as an unsupervised learning problem. In practice however, one may have---in addition to a large set of unlabeled samples---access to a small pool of labeled samples, e.g. a subset verified by some domain expert as being normal or anomalous. Semi-supervised approaches to anomaly detection make use of such labeled data to improve detection performance. Few deep semi-supervised approaches to anomaly detection have been proposed so far and those that exist are domain-specific. In this work, we present Deep SAD, an end-to-end methodology for deep semi-supervised anomaly detection. Using an information-theoretic perspective on anomaly detection, we derive a loss motivated by the idea that the entropy for the latent distribution of normal data should be lower than the entropy of the anomalous distribution. We demonstrate in extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10 along with other anomaly detection benchmark datasets that our approach is on par or outperforms shallow, hybrid, and deep competitors, even when provided with only few labeled training data.

Connected through hyperedge HE-1 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection"):
  with members:
    - "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"
    - "deep anomaly detection using geometric transformations"
    - "latent space autoregression for novelty detection"
    - "spectral normalization for generative adversarial networks"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-2 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification"):
  with members:
    - "robust anomaly detection in images using adversarial autoencoders"
    - "efficient gan based anomaly detection"
    - "deep anomaly detection using geometric transformations"
    - "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-3 (induced by source paper "deep weakly supervised anomaly detection"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "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"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-4 (induced by source paper "drocc deep robust one class classification"):
  with members:
    - "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 using geometric transformations"
    - "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-5 (induced by source paper "novelty detection via blurring"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "efficient gan based anomaly detection"
    - "exploration by random network distillation"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "autoaugment learning augmentation policies from data"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"

Connected through hyperedge HE-6 (induced by source paper "deep variational semi supervised novelty detection"):
  with members:
    - "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"
    - "deep structured energy based models for anomaly 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)