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 "attribute guided coupled gan for cross resolution face recognition"):
  Abstract: In this paper, we propose a novel attribute-guided cross-resolution (low-resolution to high-resolution) face recognition framework that leverages a coupled generative adversarial network (GAN) structure with adversarial training to find the hidden relationship between the low-resolution and high-resolution images in a latent common embedding subspace. The coupled GAN framework consists of two sub-networks, one dedicated to the low-resolution domain and the other dedicated to the high-resolution domain. Each sub-network aims to find a projection that maximizes the pair-wise correlation between the two feature domains in a common embedding subspace. In addition to projecting the images into a common subspace, the coupled network also predicts facial attributes to improve the cross-resolution face recognition. Specifically, our proposed coupled framework exploits facial attributes to further maximize the pair-wise correlation by implicitly matching facial attributes of the low and high-resolution images during the training, which leads to a more discriminative embedding subspace resulting in performance enhancement for cross-resolution face recognition. The efficacy of our approach compared with the state-of-the-art is demonstrated using the LFWA, Celeb-A, SCFace and UCCS 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 "deep sparse band selection for hyperspectral face recognition")
  Member "on low resolution face recognition in the wild comparisons and new techniques":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning when and where to zoom with deep reinforcement learning")
  Member "learning to authenticate with deep multibiometric hashing and neural network decoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "defending against adversarial iris examples using wavelet decomposition")
  Member "adversarial examples to fool iris recognition systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep sparse band selection for hyperspectral face recognition")
  Member "low resolution face recognition in the wild via selective knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph based knowledge distillation by multi head self attention network")
  Member "unsupervised image to image translation using domain specific variational information bound":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "deep sparse band selection for hyperspectral face recognition")
  Member "style and content disentanglement in generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sketch to art synthesizing stylized art images from sketches")

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)