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: "camera style adaptation for person re identification"
  Abstract: Being a cross-camera retrieval task, person re-identification suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle) adaptation. CamStyle can serve as a data augmentation approach that smooths the camera style disparities. Specifically, with CycleGAN, labeled training images can be style-transferred to each camera, and, along with the original training samples, form the augmented training set. This method, while increasing data diversity against over-fitting, also incurs a considerable level of noise. In the effort to alleviate the impact of noise, the label smooth regularization (LSR) is adopted. The vanilla version of our method (without LSR) performs reasonably well on few-camera systems in which over-fitting often occurs. With LSR, we demonstrate consistent improvement in all systems regardless of the extent of over-fitting. We also report competitive accuracy compared with the state of the art.

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"
    - "person re identification by camera correlation aware feature augmentation"

Connected through hyperedge HE-2 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "recover and identify a generative dual model for cross resolution person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "improving person re identification by attribute and identity learning"
    - "cgans with projection discriminator"
    - "multi level factorisation net for person re identification"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "joint discriminative and generative learning for person re identification"
    - "consistency regularization for generative adversarial networks"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "semantic image synthesis with spatially adaptive normalization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-4 (induced by source paper "cross modality paired images generation for rgb infrared person re identification"):
  with members:
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "in defense of the triplet loss for person re identification"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "zm net real time zero shot image manipulation network"
    - "disentangled person image generation"
    - "least squares generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "learning to adapt invariance in memory for person re identification"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "joint discriminative and generative learning for person re identification"
    - "linkage based face clustering via graph convolution network"
    - "unsupervised tracklet person re identification"
    - "long term feature banks for detailed video understanding"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "harmonious attention network for person re identification"
    - "open set domain adaptation by backpropagation"

Connected through hyperedge HE-6 (induced by source paper "attention a big surprise for cross domain person re identification"):
  with members:
    - "eanet enhancing alignment for cross domain person re identification"
    - "towards good practices on building effective cnn baseline model for person re identification"
    - "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"
    - "re identification with consistent attentive siamese networks"
    - "attention aware compositional network for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"

Connected through hyperedge HE-7 (induced by source paper "adaptive exploration for unsupervised person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "auto reid searching for a part aware convnet for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"

Connected through hyperedge HE-8 (induced by source paper "gan based pose aware regulation for video based person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "deep cosine metric learning for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep metric learning by online soft mining and class aware attention"
    - "crossing generative adversarial networks for cross view person re identification"
    - "part aligned bilinear representations for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"

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)