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 using quadruple directional deep learning features"
  Abstract: In order to resist the adverse effect of viewpoint variations for improving vehicle re-identification performance, we design quadruple directional deep learning networks to extract quadruple directional deep learning features (QD-DLF) of vehicle images. The quadruple directional deep learning networks are with similar overall architecture, including the same basic deep learning architecture but different directional feature pooling layers. Specifically, the same basic deep learning architecture is a shortly and densely connected convolutional neural network to extract basic feature maps of an input square vehicle image in the first stage. Then, the quadruple directional deep learning networks utilize different directional pooling layers, i.e., horizontal average pooling (HAP) layer, vertical average pooling (VAP) layer, diagonal average pooling (DAP) layer and anti-diagonal average pooling (AAP) layer, to compress the basic feature maps into horizontal, vertical, diagonal and anti-diagonal directional feature maps, respectively. #R##N#Finally, these directional feature maps are spatially normalized and concatenated together as a quadruple directional deep learning feature for vehicle re-identification. Extensive experiments on both VeRi and VehicleID databases show that the proposed QD-DLF approach outperforms multiple state-of-the-art vehicle re-identification methods.

Connected through hyperedge HE-1 (induced by source paper "robust extensible and fast teamed classifiers for vehicle tracking and vehicle re id in multi camera networks"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "towards universal object detection by domain attention"
    - "assed a framework for identifying physical events through adaptive social sensor data filtering"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"
    - "ram a region aware deep model for vehicle re identification"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"

Connected through hyperedge HE-2 (induced by source paper "attribute guided feature learning network for vehicle re identification"):
  with members:
    - "a dual path model with adaptive attention for vehicle re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "cross entropy adversarial view adaptation for person re identification"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "improving person re identification by attribute and identity learning"

Connected through hyperedge HE-3 (induced by source paper "small accurate and fast vehicle re id on the edge the safr approach"):
  with members:
    - "impact of fully connected layers on performance of convolutional neural networks for image classification"
    - "the state of sparsity in deep neural networks"
    - "ram a region aware deep model for vehicle re identification"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "exploring sparsity in recurrent neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-4 (induced by source paper "robust extensible and fast teamed classifiers for vehicle tracking in multi camera networks"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "towards universal object detection by domain attention"
    - "assed a framework for identifying physical events through adaptive social sensor data filtering"
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "domain adaptive attention model for unsupervised cross domain person re identification"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "do imagenet classifiers generalize to imagenet"
    - "bag of tricks and a strong baseline for deep person re identification"

Connected through hyperedge HE-5 (induced by source paper "eliminating cross camera bias for vehicle re identification"):
  with members:
    - "few shot deep adversarial learning for video based person re identification"
    - "a dual path model with adaptive attention for vehicle re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "cross entropy adversarial view adaptation for person re identification"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "improving person re identification by attribute and identity learning"

Connected through hyperedge HE-6 (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"
    - "vehicle re identification in aerial imagery dataset and approach"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "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-7 (induced by source paper "improving vehicle re identification using cnn latent spaces metrics comparison and track to track extension"):
  with members:
    - "ram a region aware deep model for vehicle re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "sift meets cnn a decade survey of instance retrieval"
    - "a survey of sparse representation algorithms and applications"
    - "a large scale car dataset for fine grained categorization and verification"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-8 (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 in aerial imagery dataset and approach"
    - "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)