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: "deep learning prototype domains for person re identification"
  Abstract: Person re-identification (re-id) is the task of matching multiple occurrences of the same person from different cameras, poses, lighting conditions, and a multitude of other factors which alter the visual appearance. Typically, this is achieved by learning either optimal features or matching metrics which are adapted to specific pairs of camera views dictated by the pairwise labelled training datasets. In this work, we formulate a deep learning based novel approach to automatic prototype-domain discovery for domain perceptive (adaptive) person re-id (rather than camera pair specific learning) for any camera views scalable to new unseen scenes without training data. We learn a separate re-id model for each of the discovered prototype-domains and during model deployment, use the person probe image to select automatically the model of the closest prototype domain. Our approach requires neither supervised nor unsupervised domain adaptation learning, i.e. no data available from the target domains. We evaluate extensively our model under realistic re-id conditions using automatically detected bounding boxes with low-resolution and partial occlusion. We show that our approach outperforms most of the state-of-the-art supervised and unsupervised methods on the latest CUHK-SYSU and PRW benchmarks.

Connected through hyperedge HE-1 (induced by source paper "spherereid deep hypersphere manifold embedding for person re identification"):
  with members:
    - "harmonious attention network for person re identification"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep mutual learning"
    - "camera style adaptation for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "neural person search machines"

Connected through hyperedge HE-2 (induced by source paper "scpnet spatial channel parallelism network for joint holistic and partial person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "person re identification by camera correlation aware feature augmentation"
    - "harmonious attention network for person re identification"
    - "re id done right towards good practices for person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "in defense of the triplet loss for person re identification"
    - "scalable person re identification on supervised smoothed manifold"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "learning deep context aware features over body and latent parts for person re identification"):
  with members:
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "dari distance metric and representation integration for person verification"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "scalable metric learning via weighted approximate rank component analysis"
    - "a richly annotated dataset for pedestrian attribute recognition"

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)