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: "a comprehensive analysis of deep learning based representation for face recognition"
  Abstract: Deep learning based approaches have been dominating the face recognition field due to the significant performance improvement they have provided on the challenging wild datasets. These approaches have been extensively tested on such unconstrained datasets, on the Labeled Faces in the Wild and YouTube Faces, to name a few. However, their capability to handle individual appearance variations caused by factors such as head pose, illumination, occlusion, and misalignment has not been thoroughly assessed till now. In this paper, we present a comprehensive study to evaluate the performance of deep learning based face representation under several conditions including the varying head pose angles, upper and lower face occlusion, changing illumination of different strengths, and misalignment due to erroneous facial feature localization. Two successful and publicly available deep learning models, namely VGG-Face and Lightened CNN have been utilized to extract face representations. The obtained results show that although deep learning provides a powerful representation for face recognition, it can still benefit from preprocessing, for example, for pose and illumination normalization in order to achieve better performance under various conditions. Particularly, if these variations are not included in the dataset used to train the deep learning model, the role of preprocessing becomes more crucial. Experimental results also show that deep learning based representation is robust to misalignment and can tolerate facial feature localization errors up to 10% of the interocular distance.

Connected through hyperedge HE-1 (induced by source paper "face recognition a novel multi level taxonomy based survey"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "ring loss convex feature normalization for face recognition"
    - "a double deep spatio angular learning framework for light field based face recognition"
    - "face recognition via centralized coordinate learning"
    - "nonlinear 3d face morphable model"

Connected through hyperedge HE-2 (induced by source paper "understanding unconventional preprocessors in deep convolutional neural networks for face identification"):
  with members:
    - "a survey on deep transfer learning"
    - "ring loss convex feature normalization for face recognition"
    - "expressing facial structure and appearance information in frequency domain for face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "how image degradations affect deep cnn based face recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "understanding how image quality affects deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "an experimental evaluation of covariates effects on unconstrained face verification"):
  with members:
    - "the do s and don ts for cnn based face verification"
    - "l2 constrained softmax loss for discriminative face verification"
    - "a proximity aware hierarchical clustering of faces"
    - "level playing field for million scale face recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "triplet probabilistic embedding for face verification and clustering"
    - "an all in one convolutional neural network for face analysis"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"

Connected through hyperedge HE-4 (induced by source paper "exploring factors for improving low resolution face recognition"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "squeeze and excitation networks"
    - "studying very low resolution recognition using deep networks"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "deepid3 face recognition with very deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "a study on various state of the art of the art face recognition system using deep learning techniques"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "unconstrained face verification using deep cnn features"
    - "pcanet a simple deep learning baseline for image classification"
    - "deep learning multi view representation for face recognition"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-6 (induced by source paper "a double deep spatio angular learning framework for light field based face recognition"):
  with members:
    - "face recognition a novel multi level taxonomy based survey"
    - "deep face recognition a survey"
    - "personalized age progression with bi level aging dictionary learning"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "regularizing and optimizing lstm language models"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "improving shadow suppression for illumination robust face recognition"

Connected through hyperedge HE-7 (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 unified alternating direction method of multipliers by majorization minimization"
    - "regularized robust coding for face recognition"

Connected through hyperedge HE-8 (induced by source paper "empirically analyzing the effect of dataset biases on deep face recognition systems"):
  with members:
    - "dynamic routing between capsules"
    - "opening the black box of deep neural networks via information"
    - "morphable face models an open framework"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "group equivariant convolutional networks"
    - "a lightened cnn for deep face representation"
    - "facenet a unified embedding for face recognition and clustering"
    - "an empirical evaluation of current convolutional architectures ability to manage nuisance location and scale variability"

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)