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: "directional statistics based deep metric learning for image classification and retrieval"
  Abstract: Deep distance metric learning (DDML), which is proposed to learn image similarity metrics in an end-to-end manner based on the convolution neural network, has achieved encouraging results in many computer vision tasks.$L2$-normalization in the embedding space has been used to improve the performance of several DDML methods. However, the commonly used Euclidean distance is no longer an accurate metric for $L2$-normalized embedding space, i.e., a hyper-sphere. Another challenge of current DDML methods is that their loss functions are usually based on rigid data formats, such as the triplet tuple. Thus, an extra process is needed to prepare data in specific formats. In addition, their losses are obtained from a limited number of samples, which leads to a lack of the global view of the embedding space. In this paper, we replace the Euclidean distance with the cosine similarity to better utilize the $L2$-normalization, which is able to attenuate the curse of dimensionality. More specifically, a novel loss function based on the von Mises-Fisher distribution is proposed to learn a compact hyper-spherical embedding space. Moreover, a new efficient learning algorithm is developed to better capture the global structure of the embedding space. Experiments for both classification and retrieval tasks on several standard datasets show that our method achieves state-of-the-art performance with a simpler training procedure. Furthermore, we demonstrate that, even with a small number of convolutional layers, our model can still obtain significantly better classification performance than the widely used softmax loss.

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 "deep learning on small datasets without pre training using cosine loss"):
  with members:
    - "hierarchy based image embeddings for semantic image retrieval"
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "tafe net task aware feature embeddings for low shot learning"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "bag of tricks for image classification with convolutional neural networks"
    - "cosface large margin cosine loss for deep face recognition"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (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"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "sosnet second order similarity regularization for local descriptor learning"):
  with members:
    - "local descriptors optimized for average precision"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "learning spread out local feature descriptors"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "spatial transformer networks"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "repmet representative based metric learning for classification and one shot object detection"):
  with members:
    - "few example object detection with model communication"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "deep meta learning learning to learn in the concept space"
    - "low shot learning from imaginary data"
    - "zero shot object detection"
    - "learning transferable architectures for scalable image recognition"
    - "deep metric learning with angular loss"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-6 (induced by source paper "cosface large margin cosine loss for deep face recognition"):
  with members:
    - "island loss for learning discriminative features in facial expression recognition"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "von mises fisher mixture model based deep learning application to face verification"
    - "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-7 (induced by source paper "a tutorial on distance metric learning mathematical foundations algorithms and experiments"):
  with members:
    - "a snapshot on nonstandard supervised learning problems taxonomy relationships problem transformations and algorithm adaptations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "a survey on metric learning for feature vectors and structured data"
    - "towards ultrahigh dimensional feature selection for big data"

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)