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 "fawkes protecting personal privacy against unauthorized deep learning models"):
  Abstract: Today's proliferation of powerful facial recognition models poses a real threat to personal privacy. As this http URL demonstrated, anyone can canvas the Internet for data, and train highly accurate facial recognition models of us without our knowledge. We need tools to protect ourselves from unauthorized facial recognition systems and their numerous potential misuses. Unfortunately, work in related areas are limited in practicality and effectiveness. In this paper, we propose Fawkes, a system that allow individuals to inoculate themselves against unauthorized facial recognition models. Fawkes achieves this by helping users adding imperceptible pixel-level changes (we call them "cloaks") to their own photos before publishing them online. When collected by a third-party "tracker" and used to train facial recognition models, these "cloaked" images produce functional models that consistently misidentify the user. We experimentally prove that Fawkes provides 95+% protection against user recognition regardless of how trackers train their models. Even when clean, uncloaked images are "leaked" to the tracker and used for training, Fawkes can still maintain a 80+% protection success rate. In fact, we perform real experiments against today's state-of-the-art facial recognition services and achieve 100% success. Finally, we show that Fawkes is robust against a variety of countermeasures that try to detect or disrupt cloaks.

Includes members:
  Member "strong baseline defenses against clean label poisoning attacks":
    also connected through other hyperedges:
      (none)
  Member "anonymousnet natural face de identification with measurable privacy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards privacy protection by generating adversarial identity masks")
  Member "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "design and interpretation of universal adversarial patches in face detection")
  Member "fooling automated surveillance cameras adversarial patches to attack person detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "making an invisibility cloak real world adversarial attacks on object detectors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems")
  Member "advhat real world adversarial attack on arcface face id system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "using honeypots to catch adversarial attacks on neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hidden trigger backdoor attacks")
  Member "detecting backdoor attacks on deep neural networks by activation clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")

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)