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: "vehicle re identification in aerial imagery dataset and approach"
  Abstract: In this work, we construct a large-scale dataset for vehicle re-identification (ReID), which contains 137k images of 13k vehicle instances captured by UAV-mounted cameras. To our knowledge, it is the largest UAV-based vehicle ReID dataset. To increase intra-class variation, each vehicle is captured by at least two UAVs at different locations, with diverse view-angles and flight-altitudes. We manually label a variety of vehicle attributes, including vehicle type, color, skylight, bumper, spare tire and luggage rack. Furthermore, for each vehicle image, the annotator is also required to mark the discriminative parts that helps them to distinguish this particular vehicle from others. Besides the dataset, we also design a specific vehicle ReID algorithm to make full use of the rich annotation information. It is capable of explicitly detecting discriminative parts for each specific vehicle and significantly outperforms the evaluated baselines and state-of-the-art vehicle ReID approaches.

Connected through hyperedge HE-1 (induced by source paper "stripe based and attribute aware network a two branch deep model for vehicle re identification"):
  with members:
    - "a dual path model with adaptive attention for vehicle re identification"
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "attributes guided feature learning for vehicle re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "vehicle re identification using quadruple directional deep learning features"
    - "re id done right towards good practices for person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "part based deep hashing for large scale person re identification"

Connected through hyperedge HE-2 (induced by source paper "part guided attention learning for vehicle re identification"):
  with members:
    - "a dual path model with adaptive attention for vehicle re identification"
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "divide and conquer the embedding space for metric learning"
    - "pvss a progressive vehicle search system for video surveillance networks"
    - "vehicle re identification using quadruple directional deep learning features"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"

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)