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: "naive deep face recognition touching the limit of lfw benchmark or not"
  Abstract: Face recognition performance improves rapidly with the recent deep learning technique developing and underlying large training dataset accumulating. In this paper, we report our observations on how big data impacts the recognition performance. According to these observations, we build our Megvii Face Recognition System, which achieves 99.50% accuracy on the LFW benchmark, outperforming the previous state-of-the-art. Furthermore, we report the performance in a real-world security certification scenario. There still exists a clear gap between machine recognition and human performance. We summarize our experiments and present three challenges lying ahead in recent face recognition. And we indicate several possible solutions towards these challenges. We hope our work will stimulate the community's discussion of the difference between research benchmark and real-world applications.

Connected through hyperedge HE-1 (induced by source paper "person recognition in personal photo collections"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "pose aware person recognition"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "robust depth based person re identification"
    - "level playing field for million scale face recognition"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-2 (induced by source paper "joint face alignment and 3d face reconstruction with application to face recognition"):
  with members:
    - "representation learning by rotating your faces"
    - "densereg fully convolutional dense shape regression in the wild"
    - "nonlinear 3d face morphable model"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "pose invariant face alignment with a single cnn"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "robust face alignment using a mixture of invariant experts"
    - "fitting a 3d morphable model to edges a comparison between hard and soft correspondences"

Connected through hyperedge HE-3 (induced by source paper "face hallucination by attentive sequence optimization with reinforcement learning"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "facial landmark machines a backbone branches architecture with progressive representation learning"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "attention aware face hallucination via deep reinforcement learning"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "zero shot super resolution using deep internal learning"
    - "parallel multiscale autoregressive density estimation"

Connected through hyperedge HE-4 (induced by source paper "face recognition from traditional to deep learning methods"):
  with members:
    - "representation learning by rotating your faces"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "additive margin softmax for face verification"
    - "enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-5 (induced by source paper "generating photo realistic training data to improve face recognition accuracy"):
  with members:
    - "representation learning by rotating your faces"
    - "domain specific face synthesis for video face recognition from a single sample per person"
    - "training deep face recognition systems with synthetic data"
    - "privacy protective gan for face de identification"
    - "recent progress of face image synthesis"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (induced by source paper "towards interpretable face recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "representation learning by rotating your faces"
    - "on learning 3d face morphable model from in the wild images"
    - "gait recognition via disentangled representation learning"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for face recognition"

Connected through hyperedge HE-7 (induced by source paper "large scale bisample learning on id vs spot face recognition"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "additive margin softmax for face verification"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep hyperspherical learning"

Connected through hyperedge HE-8 (induced by source paper "query free attacks on industry grade face recognition systems under resource constraints"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial attacks on neural network policies"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "upset and angri breaking high performance image classifiers"
    - "mixup beyond empirical risk minimization"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"

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)