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 "compact surjective encoding autoencoder for unsupervised novelty detection"):
  Abstract: In unsupervised novelty detection, a model is trained solely on the in-class data, and infer to single out out-class data. Autoencoder (AE) variants aim to compactly model the in-class data to reconstruct it exclusively, differentiating it from out-class by the reconstruction error. However, imposing compactness improperly may damage in-class reconstruction and, therefore, detection performance. To solve this, we propose Compact Surjective Encoding AE (CSE-AE). In this model, the encoding of any input is constrained into a compact manifold by exploiting the deep neural net's ignorance of the unknown. Concurrently, the in-class data is surjectively encoded to the compact manifold via AE. The mechanism is realized by both GAN and its ensembled discriminative layers, and results to reconstruct the in-class exclusively. In inference, the reconstruction error of a query is measured using high-level semantics captured by the discriminator. Extensive experiments on image data show that the proposed model gives state-of-the-art performance.

Includes members:
  Member "deep semi supervised anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification")
  Member "one class convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "iot network behavioral fingerprint inference with limited network trace for cyber investigation a meta learning approach")
  Member "using self supervised learning can improve model robustness and uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "ocgan one class novelty detection using gans with constrained latent representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection")
  Member "adversarially learned one class classifier for novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection")
  Member "deep anomaly detection using geometric transformations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "latent space autoregression for novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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.LG(Machine Learning)