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 "siamese generative adversarial privatizer for biometric data"):
  Abstract: State-of-the-art machine learning algorithms can be fooled by carefully crafted adversarial examples. As such, adversarial examples present a concrete problem in AI safety. In this work we turn the tables and ask the following question: can we harness the power of adversarial examples to prevent malicious adversaries from learning sensitive information while allowing non-malicious entities to fully benefit from the utility of released datasets? To answer this question, we propose a novel Siamese Generative Adversarial Privatizer that exploits the properties of a Siamese neural network in order to find discriminative features that convey private information. When coupled with a generative adversarial network, our model is able to correctly locate and disguise sensitive information, while minimal distortion constraint prohibits the network from reducing the utility of the resulting dataset. Our method shows promising results on a biometric dataset of fingerprints.

Includes members:
  Member "detecting adversarial image examples in deep neural networks with adaptive noise reduction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non intrusive detection of adversarial deep learning attacks via observer networks")
  Member "vgan based image representation learning for privacy preserving facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to augment expressions for few shot fine grained facial expression recognition")
  Member "generative adversarial trainer defense to adversarial perturbations with gan":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distortion agnostic deep watermarking")
  Member "semi adversarial networks convolutional autoencoders for imparting privacy to face images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "privacy preserving adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning generative adversarial representations gap under fairness and censoring constraints")
  Member "connecting pixels to privacy and utility automatic redaction of private information in images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks")
  Member "context aware generative adversarial privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "adversarial transformation networks learning to generate adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")

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)