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 "deep sparse band selection for hyperspectral face recognition"):
  Abstract: Hyperspectral imaging systems collect and process information from specific wavelengths across the electromagnetic spectrum. The fusion of multi-spectral bands in the visible spectrum has been exploited to improve face recognition performance over all the conventional broad band face images. In this book chapter, we propose a new Convolutional Neural Network (CNN) framework which adopts a structural sparsity learning technique to select the optimal spectral bands to obtain the best face recognition performance over all of the spectral bands. Specifically, in this method, images from all bands are fed to a CNN, and the convolutional filters in the first layer of the CNN are then regularized by employing a group Lasso algorithm to zero out the redundant bands during the training of the network. Contrary to other methods which usually select the useful bands manually or in a greedy fashion, our method selects the optimal spectral bands automatically to achieve the best face recognition performance over all spectral bands. Moreover, experimental results demonstrate that our method outperforms state of the art band selection methods for face recognition on several publicly-available hyperspectral face image datasets.

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 "defending against adversarial iris examples using wavelet decomposition")
  Member "attribute guided coupled gan for cross resolution face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "feature level fusion from facial attributes for face recognition")
  Member "adversarial examples to fool iris recognition systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "defending against adversarial iris examples using wavelet decomposition")
  Member "defending against adversarial iris examples using wavelet decomposition":
    also connected through other hyperedges:
      (none)
  Member "unsupervised image to image translation using domain specific variational information bound":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "unsupervised facial geometry learning for sketch to photo synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attribute guided coupled gan for cross resolution face recognition")
  Member "object detection with deep learning a review":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "multibiometric secure system based on deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "segmentation aware and adaptive iris recognition")

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)