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: "cross domain knowledge transfer for person re identification"
  Abstract: Person Re-Identification (re-id) is a challenging task in computer vision, especially when there are limited training data from multiple camera views. In this paper, we pro- pose a deep learning based person re-identification method by transferring knowledge of mid-level attribute features and high-level classification features. Building on the idea that identity classification, attribute recognition and re- identification share the same mid-level semantic representations, they can be trained sequentially by fine-tuning one based on another. In our framework, we train identity classification and attribute recognition tasks from deep Convolutional Neural Network (dCNN) to learn person information. The information can be transferred to the person re-id task and improves its accuracy by a large margin. Further- more, a Long Short Term Memory(LSTM) based Recurrent Neural Network (RNN) component is extended by a spacial gate. This component is used in the re-id model to pay attention to certain spacial parts in each recurrent unit. Experimental results show that our method achieves 78.3% of rank-1 recognition accuracy on the CUHK03 benchmark.

Connected through hyperedge HE-1 (induced by source paper "alignedreid surpassing human level performance in person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "deep ranking model by large adaptive margin learning for person re identification"
    - "video based person re identification with accumulative motion context"
    - "in defense of the triplet loss for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "deep mutual learning"

Connected through hyperedge HE-2 (induced by source paper "deep feature learning via structured graph laplacian embedding for person re identification"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "pose invariant embedding for deep person re identification"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "scalable metric learning via weighted approximate rank component analysis"
    - "person re identification past present and future"
    - "top push video based 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)