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: "regularized robust coding for face recognition"
  Abstract: Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1 -norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes the computational cost of SRC very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding (IR3C) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting, and expression changes, etc.

Connected through hyperedge HE-1 (induced by source paper "biometric recognition using deep learning a survey"):
  with members:
    - "p2sgrad refined gradients for optimizing deep face models"
    - "thirdeye triplet based iris recognition without normalization"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "deepiris iris recognition using a deep learning approach"
    - "fingernet pushing the limits of fingerprint recognition using convolutional neural network"
    - "generating talking face landmarks from speech"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-2 (induced by source paper "deep micro dictionary learning and coding network"):
  with members:
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "convolutional dictionary learning acceleration and convergence"
    - "structure aware classification using supervised dictionary learning"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "greedy deep dictionary learning"
    - "discriminative bayesian dictionary learning for classification"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "convolutional clustering for unsupervised learning"

Connected through hyperedge HE-3 (induced by source paper "efficient multi domain dictionary learning with gans"):
  with members:
    - "structured analysis dictionary learning for image classification"
    - "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"
    - "a comprehensive analysis of deep learning based representation for face recognition"
    - "a unified alternating direction method of multipliers by majorization minimization"

Connected through hyperedge HE-4 (induced by source paper "an integrated inverse space sparse representation framework for tumor classification"):
  with members:
    - "gene selection with guided regularized random forest"
    - "non negative sparse coding"

Connected through hyperedge HE-5 (induced by source paper "breast tumor classification based on decision information genes and inverse projection sparse representation"):
  with members:
    - "gene selection with guided regularized random forest"
    - "non negative sparse coding"

Connected through hyperedge HE-6 (induced by source paper "robust and low rank representation for fast face identification with occlusions"):
  with members:
    - "web scale training for face identification"
    - "robust recovery of subspace structures by low rank representation"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "modal regression based atomic representation for robust face recognition"):
  with members:
    - "image set based collaborative representation for face recognition"

Connected through hyperedge HE-8 (induced by source paper "robust lstm autoencoders for face de occlusion in the wild"):
  with members:
    - "multi view intact space learning"
    - "robust and low rank representation for fast face identification with occlusions"
    - "context encoders feature learning by inpainting"
    - "unsupervised learning of video representations using lstms"
    - "show attend and tell neural image caption generation with visual attention"
    - "facenet a unified embedding for face recognition and clustering"
    - "draw a recurrent neural network for image generation"
    - "going deeper with convolutions"

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)