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 "autogan based dimension reduction for privacy preservation"):
  Abstract: Exploiting data and concurrently protecting sensitive information to whom data belongs is an emerging research area in data mining. Several methods have been introduced to protect individual privacy and at the same time maximize data utility. Unfortunately, existing techniques such as differential privacy are not effectively protecting data owner privacy in the scenarios using visualizable data (e.g., images, videos). Furthermore, such techniques usually result in low performance with a high number of queries. To address these problems, we propose a dimension reduction-based method for privacy preservation. This method generates dimensionally-reduced data for performing machine learning tasks and prevents a strong adversary from reconstructing the original data. In this paper, we first introduce a theoretical tool to evaluate dimension reduction-based privacy preserving mechanisms, then propose a non-linear dimension reduction framework using state-of-the-art neural network structures for privacy preservation. In the experiments, we test our method on popular face image datasets and show that our method can retain data utility and resist data reconstruction, thus protecting privacy.

Includes members:
  Member "towards privacy preserving visual recognition via adversarial training a pilot study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ensei efficient secure inference via frequency domain homomorphic convolution for privacy preserving visual recognition")
  Member "generative adversarial privacy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")
  Member "understanding compressive adversarial privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed generation of privacy preserving data with user customization")
  Member "cryptodl deep neural networks over encrypted data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crypto oriented neural architecture design")
  Member "deep models under the gan information leakage from collaborative deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trends and advancements in deep neural network communication")
  Member "continuous control with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "deep learning face attributes in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake 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.CR(Cryptography and Security)