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: "highly efficient regression for scalable person re identification"
  Abstract: Existing person re-identification models are poor for scaling up to large data required in real-world applications due to: (1) Complexity: They employ complex models for optimal performance resulting in high computational cost for training at a large scale; (2) Inadaptability: Once trained, they are unsuitable for incremental update to incorporate any new data available. This work proposes a truly scalable solution to re-id by addressing both problems. Specifically, a Highly Efficient Regression (HER) model is formulated by embedding the Fisher's criterion to a ridge regression model for very fast re-id model learning with scalable memory/storage usage. Importantly, this new HER model supports faster than real-time incremental model updates therefore making real-time active learning feasible in re-id with human-in-the-loop. Extensive experiments show that such a simple and fast model not only outperforms notably the state-of-the-art re-id methods, but also is more scalable to large data with additional benefits to active learning for reducing human labelling effort in re-id deployment.

Connected through hyperedge HE-1 (induced by source paper "harmonious attention network for person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"
    - "deep reinforcement learning attention selection for person re identification"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-2 (induced by source paper "person re identification in identity regression space"):
  with members:
    - "person re identification by deep joint learning of multi loss classification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning a discriminative null space for person re identification"
    - "temporal model adaptation for person re identification"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep ranking for person re identification via joint representation learning"

Connected through hyperedge HE-3 (induced by source paper "virtual cnn branching efficient feature ensemble for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "in defense of the triplet loss for person re identification"
    - "deep mutual learning"
    - "pose driven deep convolutional model for person re identification"
    - "pose invariant embedding for deep person re identification"
    - "pedestrian alignment network for large scale person re identification"
    - "svdnet for pedestrian retrieval"
    - "densely connected convolutional networks"

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)