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: "spherereid deep hypersphere manifold embedding for person re identification"
  Abstract: Abstract   Many current successful Person Re-Identification (ReID) methods train a model with the softmax loss function to classify images of different persons and obtain the feature vectors at the same time. However, the underlying feature embedding space is ignored. In this paper, we use a modified softmax function, termed Sphere Softmax, to solve the classification problem and learn a hypersphere manifold embedding simultaneously. A balanced sampling strategy is also introduced. Finally, we propose a convolutional neural network called SphereReID adopting Sphere Softmax and training a single model end-to-end with a new warming-up learning rate schedule on four challenging datasets including Market-1501, DukeMTMC-reID, CHHK-03, and CUHK-SYSU. Experimental results demonstrate that this single model outperforms the state-of-the-art methods on all four datasets without fine-tuning or re-ranking. For example, it achieves 94.4% rank-1 accuracy on Market-1501 and 83.9% rank-1 accuracy on DukeMTMC-reID. The code and trained weights of our model will be released.

Connected through hyperedge HE-1 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning"):
  with members:
    - "deep learning for person re identification a survey and outlook"
    - "rethinking person re identification with confidence"
    - "res2net a new multi scale backbone architecture"
    - "omni scale feature learning for person re identification"
    - "attention a big surprise for cross domain person re identification"
    - "searching for a robust neural architecture in four gpu hours"
    - "unsupervised person re identification by soft multilabel learning"
    - "fairest of them all establishing a strong baseline for cross domain person reid"

Connected through hyperedge HE-2 (induced by source paper "an implicit attention mechanism for deep learning pedestrian re identification frameworks"):
  with members:
    - "bag of tricks and a strong baseline for deep person re identification"
    - "triplet based deep similarity learning for person re identification"
    - "harmonious attention network for person re identification"
    - "data augmentation by pairing samples for images classification"
    - "human semantic parsing for person re identification"
    - "random erasing data augmentation"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-3 (induced by source paper "a strong baseline and batch normalization neck for deep person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "features for multi target multi camera tracking and re identification"
    - "attention aware compositional network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "attention network robustification for person reid"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "relation aware global attention"
    - "auto reid searching for a part aware convnet for person re identification"
    - "abd net attentive but diverse person re identification"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning discriminative features with multiple granularities for person re identification"

Connected through hyperedge HE-5 (induced by source paper "multi resolution overlapping stripes network for person re identification"):
  with members:
    - "auto reid searching for a part aware convnet for person re identification"
    - "pedestrian re identification based on tree branch network with local and global learning"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning incremental triplet margin for person re identification"
    - "harmonious attention network for person re identification"
    - "resource aware person re identification across multiple resolutions"
    - "random erasing data augmentation"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"

Connected through hyperedge HE-6 (induced by source paper "spectral feature transformation for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "harmonious attention network for person re identification"
    - "cosface large margin cosine loss for deep face recognition"
    - "hard aware point to set deep metric for person re identification"
    - "normalized cut loss for weakly supervised cnn segmentation"

Connected through hyperedge HE-7 (induced by source paper "learning incremental triplet margin for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "alignedreid surpassing human level performance in person re identification"
    - "deep person learning discriminative deep features for person re identification"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "training region based object detectors with online hard example mining"
    - "learning deep feature representations with domain guided dropout 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)