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: "multi scale deep learning architectures for person re identification"
  Abstract: Person Re-identification (re-id) aims to match people across non-overlapping camera views in a public space. It is a challenging problem because many people captured in surveillance videos wear similar clothes. Consequently, the differences in their appearance are often subtle and only detectable at the right location and scales. Existing re-id models, particularly the recently proposed deep learning based ones match people at a single scale. In contrast, in this paper, a novel multi-scale deep learning model is proposed. Our model is able to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for matching. The importance of different spatial locations for extracting discriminative features is also learned explicitly. Experiments are carried out to demonstrate that the proposed model outperforms the state-of-the art on a number of benchmarks

Connected through hyperedge HE-1 (induced by source paper "when person re identification meets changing clothes"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "iqiyi vid a large dataset for multi modal person identification"
    - "pyramidbox a context assisted single shot face detector"
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "domain adaptation through synthesis for unsupervised person re identification"
    - "part aligned bilinear representations for person re identification"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-2 (induced by source paper "omni scale feature learning for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "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"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "auto reid searching for a part aware convnet for person re identification"
    - "learning generalisable omni scale representations for person re identification"
    - "on the convergence of adam and beyond"

Connected through hyperedge HE-3 (induced by source paper "torchreid a library for deep learning person re identification in pytorch"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "omni scale feature learning for person re identification"
    - "on the variance of the adaptive learning rate and beyond"
    - "torchmeta a meta learning library for pytorch"
    - "simpledet a simple and versatile distributed framework for object detection and instance recognition"
    - "learning generalisable omni scale representations for person re identification"
    - "huggingface s transformers state of the art natural language processing"
    - "mmdetection open mmlab detection toolbox and benchmark"

Connected through hyperedge HE-4 (induced by source paper "interpretable and generalizable person re identification with query adaptive convolution and temporal lifting"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised tracklet person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "spatial temporal person re identification"

Connected through hyperedge HE-5 (induced by source paper "multi pseudo regularized label for generated data in person re identification"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "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"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "improved training of wasserstein gans"
    - "pose driven deep convolutional model for person re identification"

Connected through hyperedge HE-6 (induced by source paper "deep representation learning with part loss for person re identification"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "pose driven deep convolutional model for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "pose invariant embedding for deep person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "convolutional pose machines"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"

Connected through hyperedge HE-7 (induced by source paper "improved person re identification based on saliency and semantic parsing with deep neural network models"):
  with members:
    - "salient object detection a survey"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "features for multi target multi camera tracking and re identification"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "video salient object detection via fully convolutional networks"
    - "human semantic parsing for person re identification"

Connected through hyperedge HE-8 (induced by source paper "person re identification by semantic region representation and topology constraint"):
  with members:
    - "harmonious attention network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "camera style adaptation for person re identification"
    - "deep hybrid similarity learning for person re identification"
    - "part based deep hashing for large scale person re identification"
    - "pose driven deep convolutional model for person re identification"

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)