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 "towards privacy protection by generating adversarial identity masks"):
  Abstract: As billions of personal data such as photos are shared through social media and network, the privacy and security of data have drawn an increasing attention. Several attempts have been made to alleviate the leakage of identity information with the aid of image obfuscation techniques. However, most of the present results are either perceptually unsatisfactory or ineffective against real-world recognition systems. In this paper, we argue that an algorithm for privacy protection must block the ability of automatic inference of the identity and at the same time, make the resultant image natural from the users' point of view. To achieve this, we propose a targeted identity-protection iterative method (TIP-IM), which can generate natural face images by adding adversarial identity masks to conceal ones' identity against a recognition system. Extensive experiments on various state-of-the-art face recognition models demonstrate the effectiveness of our proposed method on alleviating the identity leakage of face images, without sacrificing? the visual quality of the protected images.

Includes members:
  Member "live face de identification in video":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepfakes for medical video de identification privacy protection and diagnostic information preservation")
  Member "analyzing and improving the image quality of stylegan":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "anonymousnet natural face de identification with measurable privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")
  Member "cosface large margin cosine loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "privacy protective gan for face de identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "mobilefacenets efficient cnns for accurate real time face verification on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "a hybrid model for identity obfuscation by face replacement":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")

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)