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: "learning to rank in person re identification with metric ensembles"
  Abstract: We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated. Our framework is built on the basis of multiple low-level hand-crafted and high-level visual features. We then formulate two optimization algorithms, which directly optimize evaluation measures commonly used in person re-identification, also known as the Cumulative Matching Characteristic (CMC) curve. Our new approach is practical to many real-world surveillance applications as the re-identification performance can be concentrated in the range of most practical importance. The combination of these factors leads to a person re-identification system which outperforms most existing algorithms. More importantly, we advance state-of-the-art results on person re-identification by improving the rank-$1$ recognition rates from $40\%$ to $50\%$ on the iLIDS benchmark, $16\%$ to $18\%$ on the PRID2011 benchmark, $43\%$ to $46\%$ on the VIPeR benchmark, $34\%$ to $53\%$ on the CUHK01 benchmark and $21\%$ to $62\%$ on the CUHK03 benchmark.

Connected through hyperedge HE-1 (induced by source paper "unsupervised person re identification by deep asymmetric metric embedding"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "began boundary equilibrium generative adversarial networks"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "end to end comparative attention networks for person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "unsupervised person re identification clustering and fine tuning"
    - "person transfer gan to bridge domain gap for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-2 (induced by source paper "fmt fusing multi task convolutional neural network for person search"):
  with members:
    - "neural person search machines"
    - "end to end comparative attention networks for person re identification"
    - "svdnet for pedestrian retrieval"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "joint detection and identification feature learning for person search"
    - "deep feature learning with relative distance comparison for person re identification"
    - "filtered channel features for pedestrian detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "knowledge distillation for end to end person search"):
  with members:
    - "learning context graph for person search"
    - "query guided end to end person search"
    - "person search by multi scale matching"
    - "model compression via distillation and quantization"
    - "attention aware compositional network for person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "maximum margin metric learning over discriminative nullspace for person re identification"
    - "person search via a mask guided two stream cnn model"

Connected through hyperedge HE-4 (induced by source paper "mixed high order attention network for person re identification"):
  with members:
    - "hybrid attention based decoupled metric learning for zero shot image retrieval"
    - "energy confused adversarial metric learning for zero shot image retrieval and clustering"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "harmonious attention network for person re identification"
    - "re identification with consistent attentive siamese networks"
    - "attention aware compositional network for person re identification"
    - "discriminative learning of latent features for zero shot recognition"

Connected through hyperedge HE-5 (induced by source paper "progressive learning algorithm for efficient person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "harmonious attention network for person re identification"
    - "pyramid person matching network for person re identification"
    - "re id done right towards good practices for person re identification"
    - "resource aware person re identification across multiple resolutions"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-6 (induced by source paper "domain adaptive attention model for unsupervised cross domain person re identification"):
  with members:
    - "learning deep features for one class classification"
    - "harmonious attention network for person re identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "adaptation and re identification network an unsupervised deep transfer learning approach to person re identification"
    - "in defense of the triplet loss for person re identification"
    - "camera style adaptation for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-7 (induced by source paper "self training with progressive augmentation for unsupervised cross domain person re identification"):
  with members:
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "regularizing proxies with multi adversarial training for unsupervised domain adaptive semantic segmentation"
    - "eanet enhancing alignment for cross domain person re identification"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"

Connected through hyperedge HE-8 (induced by source paper "augmented hard example mining for generalizable person re identification"):
  with members:
    - "frustratingly easy person re identification generalizing person re id in practice"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "domain adaptation through synthesis for unsupervised person re identification"
    - "in defense of the triplet loss 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)