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: "a novel visual word co occurrence model for person re identification"
  Abstract: Person re-identification aims to maintain the identity of an individual in diverse locations through different non-overlapping camera views. The problem is fundamentally challenging due to appearance variations resulting from differing poses, illumination and configurations of camera views. To deal with these difficulties, we propose a novel visual word co-occurrence model. We first map each pixel of an image to a visual word using a codebook, which is learned in an unsupervised manner. The appearance transformation between camera views is encoded by a co-occurrence matrix of visual word joint distributions in probe and gallery images. Our appearance model naturally accounts for spatial similarities and variations caused by pose, illumination & configuration change across camera views. Linear SVMs are then trained as classifiers using these co-occurrence descriptors. On the VIPeR and CUHK Campus benchmark datasets, our method achieves 83.86% and 85.49% at rank-15 on the Cumulative Match Characteristic (CMC) curves, and beats the state-of-the-art results by 10.44% and 22.27%.

Connected through hyperedge HE-1 (induced by source paper "probabilistic semantic retrieval for surveillance videos with activity graphs"):
  with members:
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "simple online and realtime tracking with a deep association metric"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "simple online and realtime tracking"
    - "fast zero shot image tagging"
    - "person re identification by discriminative selection in video ranking"
    - "evaluating two stream cnn for video classification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "crossing generative adversarial networks for cross view person re identification"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "few shot adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unsupervised image to image translation networks"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "pose driven deep convolutional model for person re identification"

Connected through hyperedge HE-3 (induced by source paper "multi scale deep learning architectures for person re identification"):
  with members:
    - "end to end comparative attention networks for person re identification"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "an enhanced deep feature representation for person re identification"
    - "end to end deep learning for person search"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "stacked attention networks for image question answering"
    - "rethinking the inception architecture for computer vision"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-4 (induced by source paper "learning correspondence structures for person re identification"):
  with members:
    - "learning a discriminative null space for 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"
    - "an enhanced deep feature representation for person re identification"
    - "deep feature learning with relative distance comparison for person re identification"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "person re identification with correspondence structure learning"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

Connected through hyperedge HE-5 (induced by source paper "gated siamese convolutional neural network architecture for human re identification"):
  with members:
    - "transferring a semantic representation for person re identification and search"
    - "learning a discriminative null space for person re identification"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "a siamese long short term memory architecture for human re identification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-6 (induced by source paper "a siamese long short term memory architecture for human re identification"):
  with members:
    - "lstm a search space odyssey"
    - "transferring a semantic representation for person re identification and search"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "learning to rank in person re identification with metric ensembles"
    - "parallel multi dimensional lstm with application to fast biomedical volumetric image segmentation"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-7 (induced by source paper "efficient training of very deep neural networks for supervised hashing"):
  with members:
    - "learning both weights and connections for efficient neural networks"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "hashing with binary autoencoders"
    - "group membership prediction"
    - "towards biologically plausible deep learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "zero shot learning via semantic similarity embedding"
    - "an exploration of parameter redundancy in deep networks with circulant projections"

Connected through hyperedge HE-8 (induced by source paper "zero shot learning via semantic similarity embedding"):
  with members:
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "probabilistic zero shot classification with semantic rankings"
    - "group membership prediction"
    - "evaluation of output embeddings for fine grained image classification"
    - "prism person re identification via structured matching"
    - "zero shot recognition with unreliable attributes"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"

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)