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: "embedding deep metric for person re identication a study against large variations"
  Abstract: Person re-identification is challenging due to the large variations of pose, illumination, occlusion and camera view. Owing to these variations, the pedestrian data is distributed as highly-curved manifolds in the feature space, despite the current convolutional neural networks (CNN)'s capability of feature extraction. However, the distribution is unknown, so it is difficult to use the geodesic distance when comparing two samples. In practice, the current deep embedding methods use the Euclidean distance for the training and test. On the other hand, the manifold learning methods suggest to use the Euclidean distance in the local range, combining with the graphical relationship between samples, for approximating the geodesic distance. From this point of view, selecting suitable positive i.e. intra-class) training samples within a local range is critical for training the CNN embedding, especially when the data has large intra-class variations. In this paper, we propose a novel moderate positive sample mining method to train robust CNN for person re-identification, dealing with the problem of large variation. In addition, we improve the learning by a metric weight constraint, so that the learned metric has a better generalization ability. Experiments show that these two strategies are effective in learning robust deep metrics for person re-identification, and accordingly our deep model significantly outperforms the state-of-the-art methods on several benchmarks of person re-identification. Therefore, the study presented in this paper may be useful in inspiring new designs of deep models for person re-identification.

Connected through hyperedge HE-1 (induced by source paper "instance cross entropy for deep metric learning"):
  with members:
    - "ranked list loss for deep metric learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "divide and conquer the embedding space for metric learning"
    - "deep metric learning beyond binary supervision"
    - "decoupled networks"
    - "deep randomized ensembles for metric learning"

Connected through hyperedge HE-2 (induced by source paper "learning feature aggregation in temporal domain for re identification"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "person re identification via recurrent feature aggregation"
    - "in defense of the triplet loss for person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "quality aware network for set to set recognition"

Connected through hyperedge HE-3 (induced by source paper "attention driven person re identification"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "deep representation learning with part loss for person re identification"
    - "person re identification with metric learning using privileged information"
    - "improving person re identification by attribute and identity learning"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-4 (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-5 (induced by source paper "support neighbor loss for person re identification"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "deeply learned part aligned representations for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "deep mutual learning"
    - "camera style adaptation for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"

Connected through hyperedge HE-6 (induced by source paper "crossing generative adversarial networks for cross view person re identification"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "few shot adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unsupervised image to image translation networks"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "pose driven deep convolutional model for person re identification"

Connected through hyperedge HE-7 (induced by source paper "deep adaptive feature embedding with local sample distributions for person re identification"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "re ranking person re identification with k reciprocal encoding"
    - "structured deep hashing with convolutional neural networks for fast person re identification"
    - "hcp a flexible cnn framework for multi label image classification"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning a discriminative null space for person re identification"
    - "deep ranking for person re identification via joint representation learning"

Connected through hyperedge HE-8 (induced by source paper "cluster loss for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "in defense of the triplet loss for person re identification"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"
    - "transferring a semantic representation for person re identification and search"
    - "learning deep context aware features over body and latent parts 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)