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: "multichannel kernel canonical correlation analysis for cross view person reidentification"
  Abstract: In this article, we introduce a method to overcome one of the main challenges of person reidentification in multicamera networks, namely cross-view appearance changes. The proposed solution addresses the extreme variability of person appearance in different camera views by exploiting multiple feature representations. For each feature, kernel canonical correlation analysis with different kernels is employed to learn several projection spaces in which the appearance correlation between samples of the same person observed from different cameras is maximized. An iterative logistic regression is finally used to select and weight the contributions of each projection and perform the matching between the two views. Experimental evaluation shows that the proposed solution obtains comparable performance on the VIPeR and PRID 450s datasets and improves on the PRID and CUHK01 datasets with respect to the state of the art.

Connected through hyperedge HE-1 (induced by source paper "multi view common component discriminant analysis for cross view classification"):
  with members:
    - "multi view intact space learning"
    - "generalized multi view embedding for visual recognition and cross modal retrieval"
    - "multi view hybrid embedding a divide and conquer approach"
    - "adversarial discriminative domain adaptation"
    - "person re identification by unsupervised video matching"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-2 (induced by source paper "a discriminatively learned cnn embedding for person reidentification"):
  with members:
    - "semisupervised feature analysis by mining correlations among multiple tasks"
    - "enhancing person re identification in a self trained subspace"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "deep attributes driven multi camera person re identification"

Connected through hyperedge HE-3 (induced by source paper "enhancing person re identification in a self trained subspace"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "transferring a semantic representation for person re identification and search"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "deep ranking for person re identification via joint representation learning"
    - "person re identification past present and future"
    - "learning to rank in person re identification with metric ensembles"

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)