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: "von mises fisher mixture model based deep learning application to face verification"
  Abstract: A number of pattern recognition tasks, \textit{e.g.}, face verification, can be boiled down to classification or clustering of unit length directional feature vectors whose distance can be simply computed by their angle. In this paper, we propose the von Mises-Fisher (vMF) mixture model as the theoretical foundation for an effective deep-learning of such directional features and derive a novel vMF Mixture Loss and its corresponding vMF deep features. The proposed vMF feature learning achieves the characteristics of discriminative learning, \textit{i.e.}, compacting the instances of the same class while increasing the distance of instances from different classes. Moreover, it subsumes a number of popular loss functions as well as an effective method in deep learning, namely normalization. We conduct extensive experiments on face verification using 4 different challenging face datasets, \textit{i.e.}, LFW, YouTube faces, CACD and IJB-A. Results show the effectiveness and excellent generalization ability of the proposed approach as it achieves state-of-the-art results on the LFW, YouTube faces and CACD datasets and competitive results on the IJB-A dataset.

Connected through hyperedge HE-1 (induced by source paper "directional deep embedding and appearance learning for fast video object segmentation"):
  with members:
    - "deeper and wider siamese networks for real time visual tracking"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "atom accurate tracking by overlap maximization"
    - "fast and accurate online video object segmentation via tracking parts"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"

Connected through hyperedge HE-2 (induced by source paper "hyperspherical prototype networks"):
  with members:
    - "prototypical metric transfer learning for continuous speech keyword spotting with limited training data"
    - "infinite mixture prototypes for few shot learning"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "deep learning on small datasets without pre training using cosine loss"
    - "decoupled networks"
    - "fix your classifier the marginal value of training the last weight layer"
    - "cosface large margin cosine loss for deep face recognition"
    - "robust classification with convolutional prototype learning"

Connected through hyperedge HE-3 (induced by source paper "fix your features stationary and maximally discriminative embeddings using regular polytope fixed classifier networks"):
  with members:
    - "decoupled networks"
    - "fix your classifier the marginal value of training the last weight layer"
    - "learning towards minimum hyperspherical energy"
    - "additive margin softmax for face verification"
    - "improving generalization via scalable neighborhood component analysis"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-4 (induced by source paper "discriminative few shot learning based on directional statistics"):
  with members:
    - "meta learning with latent embedding optimization"
    - "cosface large margin cosine loss for deep face recognition"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "learning embedding adaptation for few shot learning"
    - "directional statistics based deep metric learning for image classification and retrieval"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with imprinted weights"

Connected through hyperedge HE-5 (induced by source paper "cosface large margin cosine loss for deep face recognition"):
  with members:
    - "directional statistics based deep metric learning for image classification and retrieval"
    - "island loss for learning discriminative features in facial expression recognition"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "l2 constrained softmax loss for discriminative face verification"
    - "squeeze and excitation networks"
    - "aggregated residual transformations for deep neural networks"
    - "range loss for deep face recognition with long tail"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "docface id document to selfie matching"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "docface matching id document photos to selfies"
    - "additive margin softmax for face verification"
    - "large scale bisample learning on id vs spot face recognition"
    - "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-7 (induced by source paper "directional statistics based deep metric learning for image classification and retrieval"):
  with members:
    - "triplet center loss for multi view 3d object retrieval"
    - "sphereface deep hypersphere embedding for face recognition"
    - "prototypical networks for few shot learning"
    - "part based deep hashing for large scale person re identification"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "fast training of triplet based deep binary embedding networks"
    - "deep metric learning via facility location"

Connected through hyperedge HE-8 (induced by source paper "spherical latent spaces for stable variational autoencoders"):
  with members:
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"
    - "style transfer from non parallel text by cross alignment"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "attention is all you need"
    - "adversarially regularized autoencoders"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "fixing a broken elbo"

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)