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 "facial attributes accuracy and adversarial robustness"):
  Abstract: Abstract   Facial attributes, emerging soft biometrics, must be automatically and reliably extracted from images in order to be usable in stand-alone systems. While recent methods extract facial attributes using deep neural networks (DNNs) trained on labeled facial attribute data, the robustness of deep attribute representations has not been evaluated. In this paper, we examine the representational stability of several approaches that recently advanced the state of the art on the CelebA benchmark by generating adversarial examples formed by adding small, non-random perturbations to inputs yielding altered classifications. We show that our fast flipping attribute (FFA) technique generates more adversarial examples than traditional algorithms, and that the adversarial robustness of DNNs varies highly between facial attributes. We also test the correlation of facial attributes and find that only for related attributes do the formed adversarial perturbations change the classification of others. Finally, we introduce the concept of natural adversarial samples, i.e., misclassified images where predictions can be corrected via small perturbations. We demonstrate that natural adversarial samples commonly occur and show that many of these images remain misclassified even with additional training epochs, even though their correct classification may require only a small adjustment to network parameters.

Includes members:
  Member "improving facial attribute prediction using semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "boosting deep face recognition via disentangling appearance and geometry")
  Member "adversarial diversity and hard positive generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems")
  Member "walk and learn facial attribute representation learning from egocentric video and contextual data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "branched multi task networks deciding what layers to share")
  Member "are facial attributes adversarially robust":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacynet semi adversarial networks for multi attribute face privacy")
  Member "adversarial machine learning at scale":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "moon a mixed objective optimization network for the recognition of facial attributes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep multi task multi label cnn for effective facial attribute classification")
  Member "adversarial manipulation of deep representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "learning social relation traits from face images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "image captioning using facial expression and attention")

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)