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: "human in the loop person re identification"
  Abstract: Current person re-identification (re-id) methods assume that (1) pre-labelled training data are available for every camera pair, (2) the gallery size for re-identification is moderate. Both assumptions scale poorly to real-world applications when camera network size increases and gallery size becomes large. Human verification of automatic model ranked re-id results becomes inevitable. In this work, a novel human-in-the-loop re-id model based on Human Verification Incremental Learning (HVIL) is formulated which does not require any pre-labelled training data to learn a model, therefore readily scalable to new camera pairs. This HVIL model learns cumulatively from human feedback to provide instant improvement to re-id ranking of each probe on-the-fly enabling the model scalable to large gallery sizes. We further formulate a Regularised Metric Ensemble Learning (RMEL) model to combine a series of incrementally learned HVIL models into a single ensemble model to be used when human feedback becomes unavailable.

Connected through hyperedge HE-1 (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-2 (induced by source paper "multi pseudo regularized label for generated data in person re identification"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "improved training of wasserstein gans"
    - "pose driven deep convolutional model for person re identification"

Connected through hyperedge HE-3 (induced by source paper "embedding human knowledge in deep neural network via attention map"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "methods for interpreting and understanding deep neural networks"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "rise randomized input sampling for explanation of black box models"
    - "smoothgrad removing noise by adding noise"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "unsupervised tracklet person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "deep association learning for unsupervised video person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "domain adaptation through synthesis for unsupervised person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"
    - "person re identification by deep joint learning of multi loss classification"

Connected through hyperedge HE-5 (induced by source paper "part aligned bilinear representations for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "person re identification by camera correlation aware feature augmentation"
    - "video based person re identification with accumulative motion context"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "deep mutual learning"

Connected through hyperedge HE-6 (induced by source paper "person search by multi scale matching"):
  with members:
    - "multi level factorisation net for person re identification"
    - "person re identification by camera correlation aware feature augmentation"
    - "harmonious attention network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "person re identification in identity regression space"
    - "unsupervised person re identification by deep learning tracklet association"
    - "in defense of the triplet loss for person re identification"
    - "deep mutual learning"

Connected through hyperedge HE-7 (induced by source paper "learning view specific deep networks for person re identification"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "scalable person re identification on supervised smoothed manifold"
    - "end to end comparative attention networks for person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning a discriminative null space for person re identification"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"

Connected through hyperedge HE-8 (induced by source paper "person re identification by camera correlation aware feature augmentation"):
  with members:
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning a discriminative null space for person re identification"
    - "person re identification by unsupervised video matching"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep ranking for person re identification via joint representation learning"
    - "a siamese long short term memory architecture for human re identification"
    - "an enhanced deep feature representation for person re identification"
    - "top push video based 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)