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: "a coarse to fine pyramidal model for person re identification via multi loss dynamic training"
  Abstract: Most existing Re-IDentification (Re-ID) methods are highly dependent on precise bounding boxes that enable images to be aligned with each other. However, due to the inevitable challenging scenarios, current detection models often output inaccurate bounding boxes yet, which inevitably worsen the performance of these Re-ID algorithms. In this paper, to relax the requirement, we propose a novel coarse-to-fine pyramid model that not only incorporates local and global information, but also integrates the gradual cues between them. The pyramid model is able to match the cues at different scales and then search for the correct image of the same identity even when the image pair are not aligned. In addition, in order to learn discriminative identity representation, we explore a dynamic training scheme to seamlessly unify two losses and extract appropriate shared information between them. Experimental results clearly demonstrate that the proposed method achieves the state-of-the-art results on three datasets and it is worth noting that our approach exceeds the current best method by 9.5% on the most challenging dataset CUHK03.

Connected through hyperedge HE-1 (induced by source paper "visual person understanding through multi task and multi dataset learning"):
  with members:
    - "improved person re identification based on saliency and semantic parsing with deep neural network models"
    - "casenet content adaptive scale interaction networks for scene parsing"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "panoptic feature pyramid networks"
    - "improving person re identification by attribute and identity learning"
    - "devil in the details towards accurate single and multiple human parsing"
    - "learning discriminative features with multiple granularities for person re identification"
    - "group normalization"

Connected through hyperedge HE-2 (induced by source paper "second order non local attention networks for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "bag of tricks for image classification with convolutional neural networks"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "compact generalized non local network"
    - "dual attention matching network for context aware feature sequence based person re identification"

Connected through hyperedge HE-3 (induced by source paper "ph gcn person re identification with part based hierarchical graph convolutional network"):
  with members:
    - "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"
    - "person re identification with deep similarity guided graph neural network"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "pyramidal person re identification via multi loss dynamic training"
    - "part aligned bilinear representations for person re identification"
    - "scalable person re identification on supervised smoothed manifold"

Connected through hyperedge HE-4 (induced by source paper "bag of tricks and a strong baseline for deep person re identification"):
  with members:
    - "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"
    - "scpnet spatial channel parallelism network for joint holistic and partial 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"

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)