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: "constrained deep metric learning for person re identification"
  Abstract: Person re-identification aims to re-identify the probe image from a given set of images under different camera views. It is challenging due to large variations of pose, illumination, occlusion and camera view. Since the convolutional neural networks (CNN) have excellent capability of feature extraction, certain deep learning methods have been recently applied in person re-identification. However, in person re-identification, the deep networks often suffer from the over-fitting problem. In this paper, we propose a novel CNN-based method to learn a discriminative metric with good robustness to the over-fitting problem in person re-identification. Firstly, a novel deep architecture is built where the Mahalanobis metric is learned with a weight constraint. This weight constraint is used to regularize the learning, so that the learned metric has a better generalization ability. Secondly, we find that the selection of intra-class sample pairs is crucial for learning but has received little attention. To cope with the large intra-class variations in pedestrian images, we propose a novel training strategy named moderate positive mining to prevent the training process from over-fitting to the extreme samples in intra-class pairs. Experiments show that our approach significantly outperforms state-of-the-art methods on several benchmarks of person re-identification.

Connected through hyperedge HE-1 (induced by source paper "a quadruplet loss for enforcing semantically coherent embeddings in multi output classification problems"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "attention set based metric learning for video face recognition"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "ssd single shot multibox detector"
    - "convolutional neural networks for attribute based active authentication on mobile devices"
    - "facenet a unified embedding for face recognition and clustering"
    - "the megaface benchmark 1 million faces for recognition at scale"

Connected through hyperedge HE-2 (induced by source paper "attention aware compositional network for person re identification"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "end to end comparative attention networks for person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "pose invariant embedding for deep person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-3 (induced by source paper "survey on deep learning techniques for person re identification task"):
  with members:
    - "deep hybrid similarity learning for person re identification"
    - "deep feature learning via structured graph laplacian embedding for person re identification"
    - "pose driven deep convolutional model for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "deep person learning discriminative deep features for person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "multi scale deep learning architectures for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"

Connected through hyperedge HE-4 (induced by source paper "parameter free spatial attention network for person re identification"):
  with members:
    - "sharp attention network via adaptive sampling for person re identification"
    - "attention aware compositional network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "random erasing data augmentation"
    - "squeeze and excitation networks"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "performance measures and a data set for multi target multi camera tracking"

Connected through hyperedge HE-5 (induced by source paper "deep feature learning via structured graph laplacian embedding for person re identification"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "pose invariant embedding for deep person re identification"
    - "cross domain knowledge transfer for person re identification"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "scalable metric learning via weighted approximate rank component analysis"
    - "person re identification past present and future"

Connected through hyperedge HE-6 (induced by source paper "deep person re identification with improved embedding and efficient training"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "svdnet for pedestrian retrieval"
    - "embedding deep metric for person re identication a study against large variations"
    - "person re identification in the wild"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning attention selection for person re identification"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "learning deep embeddings with histogram loss"
    - "end to end deep learning for person search"
    - "self critical sequence training for image captioning"
    - "hierarchical object detection with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "human in the loop person re identification"):
  with members:
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "person re identification by discriminative selection in video ranking"
    - "multiregion bilinear convolutional neural networks for person re identification"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "learning to rank in person re identification with metric ensembles"
    - "deep feature learning with relative distance comparison for person re identification"
    - "unsupervised learning of visual representations using videos"

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)