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: "dari distance metric and representation integration for person verification"
  Abstract: The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately. To explore their interaction, this work proposes an end-to-end learning framework called DARI, i.e. Distance metric And Representation Integration, and validates the effectiveness of DARI in the challenging task of person verification. Given the training images annotated with the labels, we first produce a large number of triplet units, and each one contains three images, i.e. one person and the matched/mismatch references. For each triplet unit, the distance disparity between the matched pair and the mismatched pair tends to be maximized. We solve this objective by building a deep architecture of convolutional neural networks. In particular, the Mahalanobis distance matrix is naturally factorized as one top fully-connected layer that is seamlessly integrated with other bottom layers representing the image feature. The image feature and the distance metric can be thus simultaneously optimized via the one-shot backward propagation. On several public datasets, DARI shows very promising performance on re-identifying individuals cross cameras against various challenges, and outperforms other state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised person re id differentiable graphical learning and a new benchmark"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "adaptively connected neural networks"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"

Connected through hyperedge HE-2 (induced by source paper "adaptively connected neural networks"):
  with members:
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "spatial temporal person re identification"
    - "m2m gan many to many generative adversarial transfer learning for person re identification"
    - "occluded person re identification"
    - "large scale learnable graph convolutional networks"
    - "deformable convnets v2 more deformable better results"
    - "graph based global reasoning networks"

Connected through hyperedge HE-3 (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"
    - "learning view specific deep networks 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"

Connected through hyperedge HE-4 (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-5 (induced by source paper "triplet based deep similarity learning for person re identification"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "personnet person re identification with deep convolutional neural networks"
    - "structured feature learning for pose estimation"
    - "learning to rank in person re identification with metric ensembles"
    - "a neural algorithm of artistic style"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

Connected through hyperedge HE-6 (induced by source paper "m2m gan many to many generative adversarial transfer learning for person re identification"):
  with members:
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "occluded person re identification"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "instance normalization the missing ingredient for fast stylization"
    - "deep ranking for person re identification via joint representation learning"

Connected through hyperedge HE-7 (induced by source paper "learning deep context aware features over body and latent parts for person re identification"):
  with members:
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "scalable metric learning via weighted approximate rank component analysis"
    - "a richly annotated dataset for pedestrian attribute recognition"
    - "personnet person re identification with deep convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "cross domain visual matching via generalized similarity measure and feature learning"):
  with members:
    - "deep ranking for person re identification via joint representation learning"
    - "deep feature learning with relative distance comparison for person re identification"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "deep metric learning for practical person re identification"
    - "cross modal similarity learning a low rank bilinear formulation"
    - "learning fine grained image similarity with deep ranking"
    - "fully convolutional networks for semantic segmentation"

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)