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: "when face recognition meets with deep learning an evaluation of convolutional neural networks for face recognition"
  Abstract: Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a 'good' architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible. Specifically, we use public database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing CNNs trained on private databases. We propose three CNN architectures which are the first reported architectures trained using LFW data. This paper quantitatively compares the architectures of CNNs and evaluate the effect of different implementation choices. We identify several useful properties of CNN-FRS. For instance, the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy. In addition, traditional metric learning method exploiting CNN-learned features is evaluated. Experiments show two crucial factors to good CNN-FRS performance are the fusion of multiple CNNs and metric learning. To make our work reproducible, source code and models will be made publicly available.

Connected through hyperedge HE-1 (induced by source paper "palmprint recognition in uncontrolled and uncooperative environment"):
  with members:
    - "end to end weakly supervised semantic alignment"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "palmprint recognition using deep scattering convolutional network"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "pose invariant 3d face alignment"
    - "pcanet a simple deep learning baseline for image classification"

Connected through hyperedge HE-2 (induced by source paper "deep learning for face recognition a critical analysis"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "face attention network an effective face detector for the occluded faces"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "the do s and don ts for cnn based face verification"

Connected through hyperedge HE-3 (induced by source paper "dual encoder decoder based generative adversarial networks for disentangled facial representation learning"):
  with members:
    - "representation learning by rotating your faces"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "attgan facial attribute editing by only changing what you want"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"

Connected through hyperedge HE-4 (induced by source paper "mis classified vector guided softmax loss for face recognition"):
  with members:
    - "a high efficiency framework for constructing large scale face parsing benchmark"
    - "the devil of face recognition is in the noise"
    - "ensemble soft margin softmax loss for image classification"
    - "cosface large margin cosine loss for deep face recognition"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "support vector guided softmax loss for face recognition"

Connected through hyperedge HE-5 (induced by source paper "occlusion guided compact template learning for ensemble deep network based pose invariant face recognition"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "on the intrinsic dimensionality of face representation"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "end to end 3d face reconstruction with deep neural networks"
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-6 (induced by source paper "large scale multi modal person identification in real unconstrained environments"):
  with members:
    - "iqiyi vid a large dataset for multi modal person identification"
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"
    - "pyramidbox a context assisted single shot face detector"
    - "voxceleb2 deep speaker recognition"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"

Connected through hyperedge HE-7 (induced by source paper "semantic alignment finding semantically consistent ground truth for facial landmark detection"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "look at boundary a boundary aware face alignment algorithm"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "joint multi view face alignment in the wild"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "multi fold gabor pca and ica filter convolution descriptor for face recognition"):
  with members:
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "deepid3 face recognition with very deep neural networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "a comprehensive survey on pose invariant face recognition"
    - "dctnet a simple learning free approach for face recognition"
    - "pcanet a simple deep learning baseline for image classification"
    - "effective face frontalization in unconstrained images"
    - "large scale strongly supervised ensemble metric learning with applications to face verification and retrieval"

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)