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: "learning compact appearance representation for video based person re identification"
  Abstract: This paper presents a novel approach for video-based person re-identification using multiple Convolutional Neural Networks (CNNs). Unlike previous work, we intend to extract a compact yet discriminative appearance representation from several frames rather than the whole sequence. Specifically, given a video, the representative frames are selected based on the walking profile of consecutive frames. A multiple CNN architecture incorporated with feature pooling is proposed to learn and compile the features of the selected representative frames into a compact description about the pedestrian for identification. Experiments are conducted on benchmark datasets to demonstrate the superiority of the proposed method over existing person re-identification approaches.

Connected through hyperedge HE-1 (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-2 (induced by source paper "video person re identification by temporal residual learning"):
  with members:
    - "salient object detection by lossless feature reflection"
    - "person re identification by camera correlation aware feature augmentation"
    - "person re identification via recurrent feature aggregation"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "in defense of the triplet loss for person re identification"
    - "quality aware network for set to set recognition"
    - "toward end to end face recognition through alignment learning"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"

Connected through hyperedge HE-3 (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-4 (induced by source paper "multi scale 3d convolution network for video based person re identification"):
  with members:
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "quality aware network for set to set recognition"
    - "squeeze and excitation networks"
    - "multi target tracking in multiple non overlapping cameras using constrained dominant sets"

Connected through hyperedge HE-5 (induced by source paper "lvreid person re identification with long sequence videos"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "video based person re identification with accumulative motion context"
    - "multi target tracking in multiple non overlapping cameras using constrained dominant sets"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep recurrent convolutional networks for video based person re identification an end to end approach"
    - "deep transfer learning for person re identification"
    - "deep feature learning with relative distance comparison for person re identification"

Connected through hyperedge HE-6 (induced by source paper "margin sample mining loss a deep learning based method for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "deep ranking model by large adaptive margin learning for person re identification"
    - "video based person re identification with accumulative motion context"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "in defense of the triplet loss for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "person search with natural language description"

Connected through hyperedge HE-7 (induced by source paper "alignedreid surpassing human level performance in person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "deep ranking model by large adaptive margin learning for person re identification"
    - "video based person re identification with accumulative motion context"
    - "in defense of the triplet loss for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "deep mutual learning"

Connected through hyperedge HE-8 (induced by source paper "in defense of the triplet loss for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "random erasing data augmentation"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "deep mutual learning"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "the strands project long term autonomy in everyday environments"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "learning deep context aware features over body and latent parts 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)