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: "scalable metric learning via weighted approximate rank component analysis"
  Abstract: We are interested in the large-scale learning of Mahalanobis distances, with a particular focus on person re-identification. #R##N#We propose a metric learning formulation called Weighted Approximate Rank Component Analysis (WARCA). WARCA optimizes the precision at top ranks by combining the WARP loss with a regularizer that favors orthonormal linear mappings, and avoids rank-deficient embeddings. Using this new regularizer allows us to adapt the large-scale WSABIE procedure and to leverage the Adam stochastic optimization algorithm, which results in an algorithm that scales gracefully to very large data-sets. Also, we derive a kernelized version which allows to take advantage of state-of-the-art features for re-identification when data-set size permits kernel computation. #R##N#Benchmarks on recent and standard re-identification data-sets show that our method beats existing state-of-the-art techniques both in term of accuracy and speed. We also provide experimental analysis to shade lights on the properties of the regularizer we use, and how it improves performance.

Connected through hyperedge HE-1 (induced by source paper "improving person re identification by attribute and identity learning"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "attention aware compositional network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "person search with natural language description"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "adversarial attribute image person re identification"

Connected through hyperedge HE-2 (induced by source paper "improved hard example mining by discovering attribute based hard person identity"):
  with members:
    - "fmt fusing multi task convolutional neural network for person search"
    - "pedestrian attribute recognition a survey"
    - "auto reid searching for a part aware convnet for person re identification"
    - "dense feature aggregation and pruning for rgbt tracking"
    - "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"
    - "a fast rcnn hard positive generation via adversary for object detection"

Connected through hyperedge HE-3 (induced by source paper "learning deep representations by mutual information for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "invariant information clustering for unsupervised image classification and segmentation"
    - "random erasing data augmentation"
    - "camera style adaptation for person re identification"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"

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 "multi scale body part mask guided attention for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "densereg fully convolutional dense shape regression in the wild"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "attention aware compositional network for person re identification"
    - "horizontal pyramid matching for person re identification"

Connected through hyperedge HE-6 (induced by source paper "discovering underlying person structure pattern with relative local distance for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "learning view specific deep networks for person re identification"
    - "spatial temporal person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "deep mutual learning"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "re ranking person re identification with k reciprocal encoding"

Connected through hyperedge HE-7 (induced by source paper "view confusion feature learning for person re identification"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "learning view specific deep networks for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "learning deep context aware features over body and latent parts for person re identification"

Connected through hyperedge HE-8 (induced by source paper "person re identification with metric learning using privileged information"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "re ranking person re identification with k reciprocal encoding"
    - "enhancing person re identification in a self trained subspace"
    - "end to end comparative attention networks for person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning a discriminative null space for person re identification"
    - "temporal model adaptation 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)