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 comprehensive 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 6 feature extraction algorithms and 21 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 13 other publicly available datasets: VIPeR, GRID, CAVIAR, 3DPeS, PRID, V47, WARD, SAIVT-SoftBio, 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 "metric attack and defense for person re identification"):
  with members:
    - "unsupervised tracklet person re identification"
    - "harmonious attention network for person re identification"
    - "horizontal pyramid matching for person re identification"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "adversarial binary coding for efficient person re identification"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "dynamic label graph matching for unsupervised video re identification"

Connected through hyperedge HE-2 (induced by source paper "an evaluation of deep cnn baselines for scene independent person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "person re identification by camera correlation aware feature augmentation"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "deep mutual learning"

Connected through hyperedge HE-3 (induced by source paper "in defense of the classification loss for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "deeply learned part aligned representations for person re identification"
    - "deep mutual learning"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "person re identification by deep joint learning of multi loss classification"
    - "alignedreid surpassing human level performance in person re identification"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "re id done right towards good practices for person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep mutual learning"
    - "neural person search machines"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "end to end comparative attention networks for person re identification"
    - "smart mining for deep metric learning"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "pedestrian alignment network for large scale person re identification"

Connected through hyperedge HE-6 (induced by source paper "spatial temporal person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "m2m gan many to many generative adversarial transfer learning for person re identification"
    - "occluded person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "learning deep context aware features over body and latent parts for person re identification"

Connected through hyperedge HE-7 (induced by source paper "person re identification in identity regression space"):
  with members:
    - "person re identification by deep joint learning of multi loss classification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "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"
    - "highly efficient regression for scalable person re identification"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"

Connected through hyperedge HE-8 (induced by source paper "person search in videos with one portrait through visual and temporal links"):
  with members:
    - "person recognition in personal photo collections"
    - "unifying identification and context learning for person recognition"
    - "person search with natural language description"
    - "detecting temporally consistent objects in videos through object class label propagation"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "joint detection and identification feature learning for person search"

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)