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: "deep face recognition a survey"
  Abstract: Driven by graphics processing units (GPUs), massive amounts of annotated data and more advanced algorithms, deep learning has recently taken the computer vision community by storm and has benefited real-world applications, including face recognition (FR). Deep FR methods leverage deep networks to learn more discriminative representations, significantly improving the state of the art and surpassing human performance (97.53%). In this paper, we provide a comprehensive survey of deep FR methods, including data, algorithms and scenes. First, we summarize the commonly used datasets for training and testing. Then, the data preprocessing methods are categorized into two classes: "one-to-many augmentation" and "many-to-one normalization". Second, for algorithms, we summarize different network architectures and loss functions used in the state-of-the art methods. Third, we review several scenes in deep FR, such as video FR, 3D FR and cross-age FR. Finally, some potential deficiencies of the current methods and several future directions are highlighted.

Connected through hyperedge HE-1 (induced by source paper "relational deep feature learning for heterogeneous face recognition"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "videos as space time region graphs"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-2 (induced by source paper "investigating the impact of inclusion in face recognition training data on individual face identification"):
  with members:
    - "diversity in faces"
    - "retinaface single stage dense face localisation in the wild"
    - "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"
    - "level playing field for million scale face recognition"
    - "performance measures and a data set for multi target multi camera tracking"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "region based adversarial synthesis of facial action units"):
  with members:
    - "conditional adversarial synthesis of 3d facial action units"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "attention gan for object transfiguration in wild images"
    - "ganimation anatomically aware facial animation from a single image"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "exprgan facial expression editing with controllable expression intensity"
    - "age progression regression by conditional adversarial autoencoder"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"

Connected through hyperedge HE-4 (induced by source paper "an open set recognition and few shot learning dataset for audio event classification in domestic environments"):
  with members:
    - "training neural audio classifiers with few data"
    - "ring loss convex feature normalization for face recognition"
    - "recent advances in open set recognition a survey"
    - "look listen and learn"
    - "audio scene classification with deep recurrent neural networks"
    - "icnet for real time semantic segmentation on high resolution images"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "tristounet triplet loss for speaker turn embedding"

Connected through hyperedge HE-5 (induced by source paper "attention aware fusion rgb d face recognition"):
  with members:
    - "classification of hand movements from eeg using a deep attention based lstm network"
    - "face recognition a novel multi level taxonomy based survey"
    - "capsule attention for multimodal eeg and eog spatiotemporal representation learning with application to driver vigilance estimation"
    - "robust rgb d face recognition using attribute aware loss"
    - "cbam convolutional block attention module"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "poseidon face from depth for driver pose estimation"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-6 (induced by source paper "m 2 deep id a novel model for multi view face identification using convolutional deep neural networks"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "facenet a unified embedding for face recognition and clustering"
    - "deeply coupled auto encoder networks for cross view classification"

Connected through hyperedge HE-7 (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-8 (induced by source paper "adversarial learning with margin based triplet embedding regularization"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "deep facial expression recognition a survey"
    - "attacks on state of the art face recognition using attentional adversarial attack generative network"
    - "deep defense training dnns with improved adversarial robustness"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

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)