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: "scalable person re identification on supervised smoothed manifold"
  Abstract: Most existing person re-identification algorithms either extract robust visual features or learn discriminative metrics for person images. However, the underlying manifold which those images reside on is rarely investigated. That raises a problem that the learned metric is not smooth with respect to the local geometry structure of the data manifold. #R##N#In this paper, we study person re-identification with manifold-based affinity learning, which did not receive enough attention from this area. An unconventional manifold-preserving algorithm is proposed, which can 1) make the best use of supervision from training data, whose label information is given as pairwise constraints; 2) scale up to large repositories with low on-line time complexity; and 3) be plunged into most existing algorithms, serving as a generic postprocessing procedure to further boost the identification accuracies. Extensive experimental results on five popular person re-identification benchmarks consistently demonstrate the effectiveness of our method. Especially, on the largest CUHK03 and Market-1501, our method outperforms the state-of-the-art alternatives by a large margin with high efficiency, which is more appropriate for practical applications.

Connected through hyperedge HE-1 (induced by source paper "constrained dominant sets and its applications in computer vision"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "deep constrained dominant sets for person re identification"
    - "weakly supervised semantic segmentation using constrained dominant sets"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning neural models for end to end clustering"
    - "transductive label augmentation for improved deep network learning"
    - "multi feature fusion for image retrieval using constrained dominant sets"

Connected through hyperedge HE-2 (induced by source paper "cross modality person re identification with shared specific feature transfer"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "hpiln a feature learning framework for cross modality person re identification"
    - "learning disentangled representation for robust person re identification"
    - "a new benchmark and approach for fine grained cross media retrieval"
    - "joint discriminative and generative learning for person re identification"
    - "beyond intra modality discrepancy a comprehensive survey of heterogeneous person re identification"
    - "mtfh a matrix tri factorization hashing framework for efficient cross modal retrieval"
    - "attend to the difference cross modality person re identification via contrastive correlation"

Connected through hyperedge HE-3 (induced by source paper "diversity achieving slow dropblock network for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "attention aware compositional network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "grab fast and accurate sensor processing for cashier free shopping"):
  with members:
    - "features for multi target multi camera tracking and re identification"
    - "yolov3 an incremental improvement"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "predictive corrective networks for action detection"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "action tubelet detector for spatio temporal action localization"
    - "simple online and realtime tracking with a deep association metric"
    - "scale aware face detection"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised person re id differentiable graphical learning and a new benchmark"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "adaptively connected neural networks"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"

Connected through hyperedge HE-6 (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-7 (induced by source paper "attention a big surprise for cross domain person re identification"):
  with members:
    - "eanet enhancing alignment for cross domain person re identification"
    - "towards good practices on building effective cnn baseline model for person re identification"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "re identification with consistent attentive siamese networks"
    - "attention aware compositional network for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"

Connected through hyperedge HE-8 (induced by source paper "joint discriminative and generative learning for person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "improving person re identification by attribute and identity learning"
    - "multi level factorisation net for person re identification"
    - "adversarial open world person re identification"
    - "harmonious attention network for person re identification"
    - "multimodal unsupervised image to image translation"
    - "transferable joint attribute identity deep learning for unsupervised 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)