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: "targeting ultimate accuracy face recognition via deep embedding"
  Abstract: Face Recognition has been studied for many decades. As opposed to traditional hand-crafted features such as LBP and HOG, much more sophisticated features can be learned automatically by deep learning methods in a data-driven way. In this paper, we propose a two-stage approach that combines a multi-patch deep CNN and deep metric learning, which extracts low dimensional but very discriminative features for face verification and recognition. Experiments show that this method outperforms other state-of-the-art methods on LFW dataset, achieving 99.77% pair-wise verification accuracy and significantly better accuracy under other two more practical protocols. This paper also discusses the importance of data size and the number of patches, showing a clear path to practical high-performance face recognition systems in real world.

Connected through hyperedge HE-1 (induced by source paper "boosting deep face recognition via disentangling appearance and geometry"):
  with members:
    - "robust facial landmark detection via aggregation on geometrically manipulated faces"
    - "deformable generator network unsupervised disentanglement of appearance and geometry"
    - "fast geometrically perturbed adversarial faces"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "improving facial attribute prediction using semantic segmentation"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"

Connected through hyperedge HE-2 (induced by source paper "neural architecture search for deep face recognition"):
  with members:
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "a performance comparison of loss functions for deep face recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "dropblock a regularization method for convolutional networks"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-3 (induced by source paper "diversity in faces"):
  with members:
    - "the devil of face recognition is in the noise"
    - "women also snowboard overcoming bias in captioning models"
    - "every smile is unique landmark guided diverse smile generation"
    - "arcface additive angular margin loss for deep face recognition"
    - "segmentation of both diseased and healthy skin from clinical photographs in a primary care setting"
    - "bias mitigation post processing for individual and group fairness"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (induced by source paper "striking the right balance with uncertainty"):
  with members:
    - "max margin class imbalanced learning with gaussian affinity"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning to reweight examples for robust deep learning"
    - "additive margin softmax for face verification"
    - "linear maximum margin classifier for learning from uncertain data"
    - "feature transfer learning for deep face recognition with under represented data"

Connected through hyperedge HE-5 (induced by source paper "max margin class imbalanced learning with gaussian affinity"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "feature transfer learning for deep face recognition with under represented data"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "prototypical networks for few shot learning"
    - "squeeze and excitation networks"

Connected through hyperedge HE-6 (induced by source paper "video face recognition component wise feature aggregation network c fan"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "quality aware network for set to set recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "deepvisage making face recognition simple yet with powerful generalization skills"

Connected through hyperedge HE-7 (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-8 (induced by source paper "feature level fusion from facial attributes for face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "attribute guided coupled gan for cross resolution face recognition"
    - "label propagation for deep semi supervised learning"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "transitive hashing network for heterogeneous multimedia retrieval"
    - "face attribute prediction using off the shelf cnn features"
    - "restoring highly corrupted images by impulse noise using radial basis functions interpolation"
    - "facenet a unified embedding for face recognition and clustering"

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)