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 "defending against adversarial iris examples using wavelet decomposition"):
  Abstract: Deep neural networks have presented impressive performance in biometric applications. However, their performance is highly at risk when facing carefully crafted input samples known as adversarial examples. In this paper, we present three defense strategies to detect adversarial iris examples. These defense strategies are based on wavelet domain denoising of the input examples by investigating each wavelet sub-band and removing the sub-bands that are most affected by the adversary. The first proposed defense strategy reconstructs multiple denoised versions of the input example through manipulating the mid- and high-frequency components of the wavelet domain representation of the input example and makes a decision upon the classification result of the majority of the denoised examples. The second and third proposed defense strategies aim to denoise each wavelet domain sub-band and determine the sub-bands that are most likely affected by the adversary using the reconstruction error computed for each sub-band. We test the performance of the proposed defense strategies against several attack scenarios and compare the results with five state of the art defense strategies.

Includes members:
  Member "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep sparse band selection for hyperspectral face recognition")
  Member "learning to authenticate with deep multibiometric hashing and neural network decoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attribute guided coupled gan for cross resolution face recognition")
  Member "adversarial examples to fool iris recognition systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep sparse band selection for hyperspectral face recognition")
  Member "multi level feature abstraction from convolutional neural networks for multimodal biometric identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial examples to fool iris recognition systems")
  Member "fast geometrically perturbed adversarial faces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust facial landmark detection via aggregation on geometrically manipulated faces")
  Member "generalized bilinear deep convolutional neural networks for multimodal biometric identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial examples to fool iris recognition systems")
  Member "a deep face identification network enhanced by facial attributes prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inducing optimal attribute representations for conditional gans")
  Member "adversarial examples are not easily detected bypassing ten detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)