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 "distribution discrepancy maximization for image privacy preserving"):
  Abstract: With the rapid increase in online photo sharing activities, image obfuscation algorithms become particularly important for protecting the sensitive information in the shared photos. However, existing image obfuscation methods based on hand-crafted principles are challenged by the dramatic development of deep learning techniques. To address this problem, we propose to maximize the distribution discrepancy between the original image domain and the encrypted image domain. Accordingly, we introduce a collaborative training scheme: a discriminator $D$ is trained to discriminate the reconstructed image from the encrypted image, and an encryption model $G_e$ is required to generate these two kinds of images to maximize the recognition rate of $D$, leading to the same training objective for both $D$ and $G_e$. We theoretically prove that such a training scheme maximizes two distributions' discrepancy. Compared with commonly-used image obfuscation methods, our model can produce satisfactory defense against the attack of deep recognition models indicated by significant accuracy decreases on FaceScrub, Casia-WebFace and LFW datasets.

Includes members:
  Member "multi scale face restoration with sequential gating ensemble network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "image to image translation with multi path consistency regularization")
  Member "conditional image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rsl net localising in satellite images from a radar on the ground")
  Member "fast feature fool a data independent approach to universal adversarial perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regional homogeneity towards learning transferable universal adversarial perturbations against defenses")
  Member "adversarial image perturbation for privacy protection a game theory perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "perceptual losses for real time style transfer and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "privacy preserving human activity recognition from extreme low resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an adversarial learning framework for preserving users anonymity in face based emotion recognition")
  Member "faceless person recognition privacy implications in social media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "person recognition in personal photo collections")
  Member "defeating image obfuscation with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards privacy protection by generating adversarial identity masks")

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)