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: "person re identification by camera correlation aware feature augmentation"
  Abstract: The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called  C amera co R  relation  A ware  F eature augmen T ation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "learning to adapt invariance in memory for person re identification"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "boosting self supervised learning via knowledge transfer"

Connected through hyperedge HE-2 (induced by source paper "unsupervised person re identification by deep asymmetric metric embedding"):
  with members:
    - "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"
    - "wasserstein cnn learning invariant features for nir vis face recognition"

Connected through hyperedge HE-3 (induced by source paper "scan self and collaborative attention network for video person re identification"):
  with members:
    - "person re identification in videos by analyzing spatio temporal tubes"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "learning view specific deep networks for person re identification"
    - "harmonious attention network for person re identification"
    - "efficient and deep person re identification using multi level similarity"
    - "dual attention matching network for context aware feature sequence based person re identification"

Connected through hyperedge HE-4 (induced by source paper "feature mask network for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "harmonious attention network for person re identification"
    - "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"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "person re identification by deep joint learning of multi loss classification"

Connected through hyperedge HE-5 (induced by source paper "attribute aware attention model for fine grained representation learning"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "object part attention model for fine grained image classification"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"
    - "autoencoder inspired unsupervised feature selection"
    - "end to end comparative attention networks for person re identification"

Connected through hyperedge HE-6 (induced by source paper "video person re identification by temporal residual learning"):
  with members:
    - "salient object detection by lossless feature reflection"
    - "person re identification via recurrent feature aggregation"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "in defense of the triplet loss for person re identification"
    - "quality aware network for set to set recognition"
    - "toward end to end face recognition through alignment learning"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"

Connected through hyperedge HE-7 (induced by source paper "universal person re identification"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "unsupervised multi target domain adaptation an information theoretic approach"
    - "multi target unsupervised domain adaptation without exactly shared categories"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-8 (induced by source paper "view confusion feature learning for person re identification"):
  with members:
    - "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"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"

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)