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: "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"
  Abstract: This paper addresses the problem of face recognition when there is only few, or even only a single, labeled examples of the face that we wish to recognize. Moreover, these examples are typically corrupted by nuisance variables, both linear (i.e., additive nuisance variables, such as bad lighting and wearing of glasses) and non-linear (i.e., non-additive pixel-wise nuisance variables, such as expression changes). The small number of labeled examples means that it is hard to remove these nuisance variables between the training and testing faces to obtain good recognition performance. To address the problem, we propose a method called semi-supervised sparse representation-based classification. This is based on recent work on sparsity, where faces are represented in terms of two dictionaries: a  gallery dictionary  consisting of one or more examples of each person, and a  variation dictionary  representing linear nuisance variables (e.g., different lighting conditions and different glasses). The main idea is that: 1) we use the variation dictionary to characterize the linear nuisance variables via the sparsity framework and 2) prototype face images are estimated as a gallery dictionary via a Gaussian mixture model, with mixed labeled and unlabeled samples in a semi-supervised manner, to deal with the non-linear nuisance variations between labeled and unlabeled samples. We have done experiments with insufficient labeled samples, even when there is only a single labeled sample per person. Our results on the AR, Multi-PIE, CAS-PEAL, and LFW databases demonstrate that the proposed method is able to deliver significantly improved performance over existing methods.

Connected through hyperedge HE-1 (induced by source paper "asymmetric rejection loss for fairer face recognition"):
  with members:
    - "unknown identity rejection loss utilizing unlabeled data for face recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "self training with noisy student improves imagenet classification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the devil of face recognition is in the noise"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "arcface additive angular margin loss for deep face recognition"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "semi supervised self growing generative adversarial networks for image recognition"):
  with members:
    - "noisy as clean learning unsupervised denoising from the corrupted image"
    - "nlh a blind pixel level non local method for real world image denoising"
    - "ranet ranking attention network for fast video object segmentation"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "learning view specific deep networks for person re identification"
    - "real world noisy image denoising a new benchmark"
    - "video salient object detection via fully convolutional networks"
    - "external prior guided internal prior learning for real world noisy image denoising"

Connected through hyperedge HE-3 (induced by source paper "facial feature embedded cyclegan for vis nir translation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "coupled deep learning for heterogeneous face recognition"
    - "adversarial discriminative heterogeneous face recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "not afraid of the dark nir vis face recognition via cross spectral hallucination and low rank embedding"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "a paired sparse representation model for robust face recognition from a single sample"):
  with members:
    - "domain specific face synthesis for video face recognition from a single sample per person"
    - "cosface large margin cosine loss for deep face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "effective face frontalization in unconstrained images"

Connected through hyperedge HE-5 (induced by source paper "complexface a multi representation approach for image classification with small dataset"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "deep complex networks"
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "a survey of sparse representation algorithms and applications"
    - "robust face recognition via multimodal deep face representation"
    - "sparsifying neural network connections for face recognition"
    - "deepid3 face recognition with very deep neural networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-6 (induced by source paper "domain specific face synthesis for video face recognition from a single sample per person"):
  with members:
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "do we really need to collect millions of faces for effective face recognition"
    - "learning from simulated and unsupervised images through adversarial training"
    - "face recognition using deep multi pose representations"
    - "image to image translation with conditional adversarial networks"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "learning detailed face reconstruction from a single image"
    - "effective face frontalization in unconstrained images"

Connected through hyperedge HE-7 (induced by source paper "hyperspectral image classification in the presence of noisy labels"):
  with members:
    - "superpca a superpixelwise pca approach for unsupervised feature extraction of hyperspectral imagery"

Connected through hyperedge HE-8 (induced by source paper "consensus driven propagation in massive unlabeled data for face recognition"):
  with members:
    - "the devil of face recognition is in the noise"
    - "accelerated training for massive classification via dynamic class selection"
    - "arcface additive angular margin loss for deep face recognition"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "learning transferable architectures for scalable image recognition"
    - "mix and match tuning for self supervised semantic segmentation"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"

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)