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: "active authentication using an autoencoder regularized cnn based one class classifier"
  Abstract: Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at: github.com/otkupjnoz/oc-acnn.

Connected through hyperedge HE-1 (induced by source paper "c2ae class conditioned auto encoder for open set recognition"):
  with members:
    - "one class convolutional neural network"
    - "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-2 (induced by source paper "learning deep features for one class classification"):
  with members:
    - "one class convolutional neural network"
    - "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-3 (induced by source paper "deep transfer learning for multiple class novelty detection"):
  with members:
    - "one class convolutional neural network"
    - "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-4 (induced by source paper "ocgan one class novelty detection using gans with constrained latent representations"):
  with members:
    - "one class convolutional neural network"
    - "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"

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)