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 "adversarial image perturbation for privacy protection a game theory perspective"):
  Abstract: Users like sharing personal photos with others through social media. At the same time, they might want to make automatic identification in such photos difficult or even impossible. Classic obfuscation methods such as blurring are not only unpleasant but also not as effective as one would expect. Recent studies on adversarial image perturbations (AIP) suggest that it is possible to confuse recognition systems effectively without unpleasant artifacts. However, in the presence of counter measures against AIPs, it is unclear how effective AIP would be in particular when the choice of counter measure is unknown. Game theory provides tools for studying the interaction between agents with uncertainties in the strategies. We introduce a general game theoretical framework for the user-recogniser dynamics, and present a case study that involves current state of the art AIP and person recognition techniques. We derive the optimal strategy for the user that assures an upper bound on the recognition rate independent of the recogniser's counter measure. Code is available at this https URL.

Includes members:
  Member "person recognition in personal photo collections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shape evasion preventing body shape inference of multi stage approaches")
  Member "learning deep features via congenerous cosine loss for person recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a performance comparison of loss functions for deep face recognition")
  Member "a domain based approach to social relation recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta transfer learning through hard tasks")
  Member "simple black box adversarial perturbations for deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")
  Member "assessing threat of adversarial examples on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial learning in statistical classification a comprehensive review of defenses against attacks")
  Member "faceless person recognition privacy implications in social media":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "person recognition in personal photo collections")
  Member "transferability in machine learning from phenomena to black box attacks using adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "adversarial diversity and hard positive generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems")

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)