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: "one class convolutional neural network"
  Abstract: We present a novel convolutional neural network (CNN) based approach for one-class classification. The idea is to use a zero centered Gaussian noise in the latent space as the pseudo-negative class and train the network using the cross-entropy loss to learn a good representation as well as the decision boundary for the given class. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one-class classification. The proposed one-class CNN is evaluated on the UMDAA-02 Face, Abnormality-1001, and FounderType-200 datasets. These datasets are related to a variety of one-class application problems such as user authentication, abnormality detection, and novelty detection. Extensive experiments demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. The source code is available at: github.com/otkupjnoz/oc-cnn.

Connected through hyperedge HE-1 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection"):
  with members:
    - "deep semi supervised anomaly detection"
    - "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 "iot network behavioral fingerprint inference with limited network trace for cyber investigation a meta learning approach"):
  with members:
    - "prototypical networks for few shot learning"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "text understanding from scratch"

Connected through hyperedge HE-3 (induced by source paper "c2ae class conditioned auto encoder for open set recognition"):
  with members:
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "learning deep features for one class classification"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "dafe fd density aware feature enrichment for face detection"
    - "reducing network agnostophobia"
    - "deep clustering for unsupervised learning of visual features"
    - "deep multimodal subspace clustering networks"

Connected through hyperedge HE-4 (induced by source paper "learning deep features for one class classification"):
  with members:
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "latent space autoregression for novelty detection"
    - "and autoregressive novelty detectors"
    - "anomaly detection using one class neural networks"
    - "sparse representation based open set recognition"
    - "active user authentication for smartphones a challenge data set and benchmark results"

Connected through hyperedge HE-5 (induced by source paper "deep transfer learning for multiple class novelty detection"):
  with members:
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "reducing network agnostophobia"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"
    - "densely connected convolutional networks"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-6 (induced by source paper "active authentication using an autoencoder regularized cnn based one class classifier"):
  with members:
    - "learning deep features for one class classification"
    - "adversarially learned one class classifier for novelty detection"
    - "anomaly detection using one class neural networks"
    - "abnormal event detection in videos using generative adversarial nets"
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "convolutional neural networks for attribute based active authentication on mobile devices"
    - "a learned representation for artistic style"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "ocgan one class novelty detection using gans with constrained latent representations"):
  with members:
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "learning deep features for one class classification"
    - "adversarially learned one class classifier for novelty detection"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "and autoregressive novelty detectors"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-8 (induced by source paper "one way prototypical networks"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "learning deep features for one class classification"
    - "adversarially learned one class classifier for novelty detection"
    - "gaussian prototypical networks for few shot learning on omniglot"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "prototypical networks for few shot learning"
    - "matching networks for one shot learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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.CV(Computer Vision and Pattern Recognition)