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 view specific deep networks for person re identification"
  Abstract: In recent years, a growing body of research has focused on the problem of person re-identification (re-id). The re-id techniques attempt to match the images of pedestrians from disjoint non-overlapping camera views. A major challenge of the re-id is the serious intra-class variations caused by changing viewpoints. To overcome this challenge, we propose a deep neural network-based framework which utilizes the view information in the feature extraction stage. The proposed framework learns a view-specific network for each camera view with a cross-view Euclidean constraint (CV-EC) and a cross-view center loss. We utilize the CV-EC to decrease the margin of the features between diverse views and extend the center loss metric to a view-specific version to better adapt the re-id problem. Moreover, we propose an iterative algorithm to optimize the parameters of the view-specific networks from coarse to fine. The experiments demonstrate that our approach significantly improves the performance of the existing deep networks and outperforms the state-of-the-art methods on the VIPeR, CUHK01, CUHK03, SYSU-mReId, and Market-1501 benchmarks.

Connected through hyperedge HE-1 (induced by source paper "cdpm convolutional deformable part models for semantically aligned person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "video person re identification by temporal residual learning"
    - "person re identification with metric learning using privileged information"
    - "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"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"

Connected through hyperedge HE-2 (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"
    - "person re identification by camera correlation aware feature augmentation"
    - "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-3 (induced by source paper "semi supervised self growing generative adversarial networks for image recognition"):
  with members:
    - "noisy as clean learning unsupervised denoising from the corrupted image"
    - "nlh a blind pixel level non local method for real world image denoising"
    - "ranet ranking attention network for fast video object segmentation"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "real world noisy image denoising a new benchmark"
    - "video salient object detection via fully convolutional networks"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-4 (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"
    - "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"
    - "glad global local alignment descriptor for pedestrian retrieval"

Connected through hyperedge HE-5 (induced by source paper "towards better validity dispersion based clustering for unsupervised person re identification"):
  with members:
    - "person re identification with metric learning using privileged information"
    - "feature affinity based pseudo labeling for semi supervised person re identification"
    - "deep clustering for unsupervised learning of visual features"
    - "pamm pose aware multi shot matching for improving person re identification"
    - "deep association learning for unsupervised video person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "part aligned bilinear representations for person re identification"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"

Connected through hyperedge HE-6 (induced by source paper "view confusion feature learning for person re identification"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "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)