human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "ocgan one class novelty detection using gans with constrained latent representations"):
  Abstract: We present a novel model called OCGAN for the classical problem of one-class novelty detection, where, given a set of examples from a particular class, the goal is to determine if a query example is from the same class. Our solution is based on learning latent representations of in-class examples using a denoising auto-encoder network. The key contribution of our work is our proposal to explicitly constrain the latent space to exclusively represent the given class. In order to accomplish this goal, firstly, we force the latent space to have bounded support by introducing a tanh activation in the encoder's output layer. Secondly, using a discriminator in the latent space that is trained adversarially, we ensure that encoded representations of in-class examples resemble uniform random samples drawn from the same bounded space. Thirdly, using a second adversarial discriminator in the input space, we ensure all randomly drawn latent samples generate examples that look real. Finally, we introduce a gradient-descent based sampling technique that explores points in the latent space that generate potential out-of-class examples, which are fed back to the network to further train it to generate in-class examples from those points. The effectiveness of the proposed method is measured across four publicly available datasets using two one-class novelty detection protocols where we achieve state-of-the-art results.

Includes members:
  Member "one class convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "active authentication using an autoencoder regularized cnn based one class classifier":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "c2ae class conditioned auto encoder for open set recognition")
  Member "deep transfer learning for multiple class novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi class novelty detection using mix up technique")
  Member "learning deep features for one class classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning achieves perfect anomaly detection on 108 308 retinal images including unlearned diseases")
  Member "adversarially learned one class classifier for novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "generative probabilistic novelty detection with adversarial autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memory augmented generative adversarial networks for anomaly detection")
  Member "and autoregressive novelty detectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep cnn based multi task learning for open set recognition")
  Member "unsupervised anomaly detection with generative adversarial networks to guide marker discovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")

Question: Please tell me which class the source paper 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)