human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "face synthesis for eyeglass robust face recognition"
  Abstract: In the application of face recognition, eyeglasses could significantly degrade the recognition accuracy. A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods. However, it is difficult to collect the images with and without glasses of the same identity, so that it is difficult to optimize the intra-variations caused by eyeglasses. In this paper, we propose to address this problem in a virtual synthesis manner. The high-fidelity face images with eyeglasses are synthesized based on 3D face model and 3D eyeglasses. Models based on deep learning methods are then trained on the synthesized eyeglass face dataset, achieving better performance than previous ones. Experiments on the real face database validate the effectiveness of our synthesized data for improving eyeglass face recognition performance.

Connected through hyperedge HE-1 (induced by source paper "learning meta face recognition in unseen domains"):
  with members:
    - "meta learning with implicit gradients"
    - "attentional feature pair relation networks for accurate face recognition"
    - "on first order meta learning algorithms"
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "meta transfer learning for few shot learning"
    - "dynamic few shot visual learning without forgetting"
    - "racial faces in the wild reducing racial bias by information maximization adaptation network"

Connected through hyperedge HE-2 (induced by source paper "unsupervised eyeglasses removal in the wild"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "joint discriminative and generative learning for person re identification"
    - "generating diverse high fidelity images with vq vae 2"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "multimodal unsupervised image to image translation"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-3 (induced by source paper "improving face anti spoofing by 3d virtual synthesis"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "on the learning of deep local features for robust face spoofing detection"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "learning camera viewpoint using cnn to improve 3d body pose estimation"
    - "mocap guided data augmentation for 3d pose estimation in the wild"
    - "synthesizing training images for boosting human 3d pose estimation"

Question: Please tell me which class the center node 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)