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: "an enhanced deep feature representation for person re identification"
  Abstract: Feature representation and metric learning are two critical components in person re-identification models. In this paper, we focus on the feature representation and claim that hand-crafted histogram features can be complementary to Convolutional Neural Network (CNN) features. We propose a novel feature extraction model called Feature Fusion Net (FFN) for pedestrian image representation. In FFN, back propagation makes CNN features constrained by the handcrafted features. Utilizing color histogram features (RGB, HSV, YCbCr, Lab and YIQ) and texture features (multi-scale and multi-orientation Gabor features), we get a new deep feature representation that is more discriminative and compact. Experiments on three challenging datasets (VIPeR, CUHK01, PRID450s) validates the effectiveness of our proposal.

Connected through hyperedge HE-1 (induced by source paper "learning shape representations for clothing variations in person re identification"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "recover and identify a generative dual model for cross resolution person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "rgb infrared cross modality person re identification via joint pixel and feature alignment"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "deep representation learning with part loss for person re identification"
    - "learning resolution invariant deep representations for person re identification"
    - "improving person re identification by attribute and identity learning"

Connected through hyperedge HE-2 (induced by source paper "unsupervised person re identification by deep asymmetric metric embedding"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "began boundary equilibrium generative adversarial networks"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "end to end comparative attention networks for person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "unsupervised person re identification clustering and fine tuning"
    - "person transfer gan to bridge domain gap for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-3 (induced by source paper "towards interpretable deep neural networks an exact transformation to multi class multivariate decision trees"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "visualizing and understanding convolutional networks"
    - "a system for induction of oblique decision trees"

Connected through hyperedge HE-4 (induced by source paper "pac gan an effective pose augmentation scheme for unsupervised cross view person re identification"):
  with members:
    - "few shot deep adversarial learning for video based person re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "cross entropy adversarial view adaptation for person re identification"
    - "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"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "sem gan semantically consistent image to image translation"

Connected through hyperedge HE-5 (induced by source paper "gan based pose aware regulation for video based person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "deep cosine metric learning for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep metric learning by online soft mining and class aware attention"
    - "crossing generative adversarial networks for cross view person re identification"
    - "part aligned bilinear representations for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "camera style adaptation for person re identification"

Connected through hyperedge HE-6 (induced by source paper "deep representation learning with part loss for person re identification"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "pose driven deep convolutional model for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "pose invariant embedding for deep person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "multi scale deep learning architectures for person re identification"
    - "convolutional pose machines"

Connected through hyperedge HE-7 (induced by source paper "learning feature aggregation in temporal domain for re identification"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "person re identification via recurrent feature aggregation"
    - "in defense of the triplet loss for person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "quality aware network for set to set recognition"

Connected through hyperedge HE-8 (induced by source paper "person re identification by semantic region representation and topology constraint"):
  with members:
    - "harmonious attention network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "camera style adaptation for person re identification"
    - "deep hybrid similarity learning for person re identification"
    - "part based deep hashing for large scale person re identification"
    - "pose driven deep convolutional model for person re identification"

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)