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 "learning deep features for one class classification"):
  Abstract: We present a novel deep-learning-based approach for  one-class transfer learning  in which labeled data from an unrelated task is used for feature learning in one-class classification. The proposed method operates on top of a convolutional neural network (CNN) of choice and produces descriptive features while maintaining a low intra-class variance in the feature space for the given class. For this purpose two loss functions,  compactness loss  and  descriptiveness loss,  are proposed along with a parallel CNN architecture. A template matching-based framework is introduced to facilitate the testing process. Extensive experiments on publicly available anomaly detection, novelty detection, and mobile active authentication datasets show that the proposed deep one-class (DOC) classification method achieves significant improvements over the state-of-the-art.

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 "ocgan one class novelty detection using gans with constrained latent representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "latent space autoregression for novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "and autoregressive novelty detectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep cnn based multi task learning for open set recognition")
  Member "anomaly detection using one class neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")
  Member "sparse representation based open set recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "conditional gaussian distribution learning for open set recognition")

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)