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 "one class adversarial nets for fraud detection"):
  Abstract: Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users. However, in practice, there are often no or very few records of malicious users. In this paper, we develop one-class adversarial nets (OCAN) for fraud detection using training data with only benign users. OCAN first uses LSTM-Autoencoder to learn the representations of benign users from their sequences of online activities. It then detects malicious users by training a discriminator with a complementary GAN model that is different from the regular GAN model. Experimental results show that our OCAN outperforms the state-of-the-art one-class classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.

Includes members:
  Member "false information on web and social media a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "good semi supervised learning that requires a bad gan":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial code learning for image generation")
  Member "neural machine translation and sequence to sequence models a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "an army of me sockpuppets in online discussion communities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep unified multimodal embeddings for understanding both content and users in social media networks")
  Member "spectrum based deep neural networks for fraud detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fraud detection in networks state of the art")
  Member "anyone can become a troll causes of trolling behavior in online discussions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "wikipedia vandal early detection from user behavior to user embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stre self attentive edit quality prediction in wikipedia")
  Member "photo realistic single image super resolution using a generative adversarial network":
    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)