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: "deep metric learning for practical person re identification"
  Abstract: Various hand-crafted features and metric learning methods prevail in the field of person re-identification. Compared to these methods, this paper proposes a more general way that can learn a similarity metric from image pixels directly. By using a "siamese" deep neural network, the proposed method can jointly learn the color feature, texture feature and metric in a unified framework. The network has a symmetry structure with two sub-networks which are connected by Cosine function. To deal with the big variations of person images, binomial deviance is used to evaluate the cost between similarities and labels, which is proved to be robust to outliers. #R##N#Compared to existing researches, a more practical setting is studied in the experiments that is training and test on different datasets (cross dataset person re-identification). Both in "intra dataset" and "cross dataset" settings, the superiorities of the proposed method are illustrated on VIPeR and PRID.

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 "event sequence metric learning"):
  with members:
    - "e t rnn applying deep learning to credit loan applications"
    - "metric learning for adversarial robustness"
    - "learning a unified embedding for visual search at pinterest"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "a comparison study of credit card fraud detection supervised versus unsupervised"
    - "prediction is very hard especially about conversion predicting user purchases from clickstream data in fashion e commerce"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "action quality assessment using siamese network based deep metric learning"):
  with members:
    - "what and how well you performed a multitask learning approach to action quality assessment"
    - "3d human action recognition with siamese lstm based deep metric learning"
    - "the pros and cons rank aware temporal attention for skill determination in long videos"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "who s better who s best pairwise deep ranking for skill determination"
    - "stacked hourglass networks for human pose estimation"
    - "learning to score olympic events"

Connected through hyperedge HE-4 (induced by source paper "on low resolution face recognition in the wild comparisons and new techniques"):
  with members:
    - "generative adversarial networks an overview"
    - "additive margin softmax for face verification"
    - "deep semantic face deblurring"
    - "face super resolution through wasserstein gans"
    - "deep face deblurring"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-5 (induced by source paper "hyperbolic image embeddings"):
  with members:
    - "hyperbolic attention networks"
    - "riemannian adaptive optimization methods"
    - "multi level factorisation net for person re identification"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "hyperbolic neural networks"
    - "embedding text in hyperbolic spaces"
    - "representation tradeoffs for hyperbolic embeddings"

Connected through hyperedge HE-6 (induced by source paper "multi similarity loss with general pair weighting for deep metric learning"):
  with members:
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "a zero shot framework for sketch based image retrieval"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "triplet center loss for multi view 3d object retrieval"
    - "in defense of the triplet loss for person re identification"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "a simple and effective framework for pairwise deep metric learning"):
  with members:
    - "hybrid attention based decoupled metric learning for zero shot image retrieval"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "revisiting metric learning for few shot image classification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "large scale distance metric learning with uncertainty"
    - "hard aware point to set deep metric for person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"

Connected through hyperedge HE-8 (induced by source paper "learning adaptively from the unknown for few example video person re id"):
  with members:
    - "harmonious attention network for person re identification"
    - "attention based few shot person re identification using meta learning"
    - "video based person re identification with accumulative motion context"
    - "quality aware network for set to set recognition"
    - "dynamic label graph matching for unsupervised video re identification"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "matching networks for one shot learning"
    - "deep feature learning with relative distance comparison for person re identification"

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)