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 discriminative features with multiple granularities for person re identification"
  Abstract: The combination of global and partial features has been an essential solution to improve discriminative performances in person re-identification (Re-ID) tasks. Previous part-based methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, which increases learning difficulty but not efficient or robust to scenarios with large variances. In this paper, we propose an end-to-end feature learning strategy integrating discriminative information with various granularities. We carefully design the Multiple Granularity Network (MGN), a multi-branch deep network architecture consisting of one branch for global feature representations and two branches for local feature representations. Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities. Comprehensive experiments implemented on the mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03 indicate that our method robustly achieves state-of-the-art performances and outperforms any existing approaches by a large margin. For example, on Market-1501 dataset in single query mode, we obtain a top result of Rank-1/mAP=96.6%/94.2% with this method after re-ranking.

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"
    - "spherereid deep hypersphere manifold embedding for person re identification"
    - "unsupervised person re identification by soft multilabel learning"

Connected through hyperedge HE-2 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "beyond human parts dual part aligned representations for person re identification"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "foreground aware pyramid reconstruction for alignment free occluded person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "improving person re identification by attribute and identity learning"

Connected through hyperedge HE-3 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"

Connected through hyperedge HE-4 (induced by source paper "circle loss a unified perspective of pair similarity optimization"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "softmax dissection towards understanding intra and inter class objective for embedding learning"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-5 (induced by source paper "diversity achieving slow dropblock network for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "attention aware compositional network for person re identification"
    - "scpnet spatial channel parallelism network for joint holistic and partial person re identification"

Connected through hyperedge HE-6 (induced by source paper "deep attention aware feature learning for person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "aanet attribute attention network for person re identifications"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "pyramidal person re identification via multi loss dynamic training"
    - "human semantic parsing for person re identification"

Connected through hyperedge HE-7 (induced by source paper "uncertainty aware multi shot knowledge distillation for image based object re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "harmonious attention network for person re identification"
    - "horizontal pyramid matching for person re identification"
    - "densely semantically aligned person re identification"
    - "human semantic parsing for person re identification"

Connected through hyperedge HE-8 (induced by source paper "watchdog real time vehicle tracking on geo distributed edge nodes"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "learning to multitask"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"
    - "mask r cnn"

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)