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: "a systematic evaluation and benchmark for person re identification features metrics and datasets"
  Abstract: Person re-identification (re-id) is a critical problem in video analytics applications such as security and surveillance. The public release of several datasets and code for vision algorithms has facilitated rapid progress in this area over the last few years. However, directly comparing re-id algorithms reported in the literature has become difficult since a wide variety of features, experimental protocols, and evaluation metrics are employed. In order to address this need, we present an extensive review and performance evaluation of single- and multi-shot re-id algorithms. The experimental protocol incorporates the most recent advances in both feature extraction and metric learning. To ensure a fair comparison, all of the approaches were implemented using a unified code library that includes 11 feature extraction algorithms and 22 metric learning and ranking techniques. All approaches were evaluated using a new large-scale dataset that closely mimics a real-world problem setting, in addition to 16 other publicly available datasets: VIPeR, GRID, CAVIAR, DukeMTMC4ReID, 3DPeS, PRID, V47, WARD, SAIVT-SoftBio, CUHK01, CHUK02, CUHK03, RAiD, iLIDSVID, HDA+ and Market1501. The evaluation codebase and results will be made publicly available for community use.

Connected through hyperedge HE-1 (induced by source paper "adaptive deep metric embeddings for person re identification under occlusions"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "lattice long short term memory for human action recognition"
    - "pose driven deep convolutional model for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "svdnet for pedestrian retrieval"

Connected through hyperedge HE-2 (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-3 (induced by source paper "beyond intra modality a survey of heterogeneous person re identification"):
  with members:
    - "illumination adaptive person re identification"
    - "multi level factorisation net 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"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "densely semantically aligned person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"

Connected through hyperedge HE-4 (induced by source paper "bag of negatives for siamese architectures"):
  with members:
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "efficient and deep person re identification using multi level similarity"
    - "deep metric learning with hierarchical triplet loss"
    - "human semantic parsing for person re identification"
    - "attention based ensemble for deep metric learning"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-5 (induced by source paper "enhancing the discriminative feature learning for visible thermal cross modality person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "person re identification by camera correlation aware feature augmentation"
    - "harmonious attention network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "learning two branch neural networks for image text matching tasks"
    - "coupled deep learning for heterogeneous face recognition"

Connected through hyperedge HE-6 (induced by source paper "target specific action classification for automated assessment of human motor behavior from video"):
  with members:
    - "vision based assessment of parkinsonism and levodopa induced dyskinesia with pose estimation"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "video action transformer network"
    - "non linear temporal subspace representations for activity recognition"
    - "unsupervised person re identification by deep learning tracklet association"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "background subtraction via fast robust matrix completion"
    - "posetrack a benchmark for human pose estimation and tracking"

Connected through hyperedge HE-7 (induced by source paper "appearance invariant entry exit matching using visual soft biometric traits"):
  with members:
    - "human skin detection using rgb hsv and ycbcr color models"

Connected through hyperedge HE-8 (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"

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)