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: "spatial temporal person re identification"
  Abstract: Most of current person re-identification (ReID) methods neglect a spatial-temporal constraint. Given a query image, conventional methods compute the feature distances between the query image and all the gallery images and return a similarity ranked table. When the gallery database is very large in practice, these approaches fail to obtain a good performance due to appearance ambiguity across different camera views. In this paper, we propose a novel two-stream spatial-temporal person ReID (st-ReID) framework that mines both visual semantic information and spatial-temporal information. To this end, a joint similarity metric with Logistic Smoothing (LS) is introduced to integrate two kinds of heterogeneous information into a unified framework. To approximate a complex spatial-temporal probability distribution, we develop a fast Histogram-Parzen (HP) method. With the help of the spatial-temporal constraint, the st-ReID model eliminates lots of irrelevant images and thus narrows the gallery database. Without bells and whistles, our st-ReID method achieves rank-1 accuracy of 98.1\% on Market-1501 and 94.4\% on DukeMTMC-reID, improving from the baselines 91.2\% and 83.8\%, respectively, outperforming all previous state-of-the-art methods by a large margin.

Connected through hyperedge HE-1 (induced by source paper "unity style transfer for person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "improving shape deformation in unsupervised image to image translation"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"

Connected through hyperedge HE-2 (induced by source paper "a strong baseline for fashion retrieval with person re identification models"):
  with members:
    - "fashion retrieval via graph reasoning networks on a similarity pyramid"
    - "mildnet a lightweight single scaled deep ranking architecture"
    - "auto reid searching for a part aware convnet for person re identification"
    - "learning generalisable omni scale representations for person re identification"
    - "a strong baseline and batch normalization neck for deep person re identification"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "learning embeddings for product visual search with triplet loss and online sampling"
    - "re ranking person re identification with k reciprocal encoding"

Connected through hyperedge HE-3 (induced by source paper "learning landmark guided embeddings for animal re identification"):
  with members:
    - "robust re identification of manta rays from natural markings by learning pose invariant embeddings"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "amur tiger re identification in the wild"
    - "survey on deep learning techniques for person re identification task"
    - "aligned to the object not to the image a unified pose aligned representation for fine grained recognition"
    - "in defense of the triplet loss for person re identification"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "pose driven deep convolutional model for person re identification"

Connected through hyperedge HE-4 (induced by source paper "interpretable and generalizable person re identification with query adaptive convolution and temporal lifting"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised tracklet person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive 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"

Connected through hyperedge HE-5 (induced by source paper "adaptively connected neural networks"):
  with members:
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "m2m gan many to many generative adversarial transfer learning for person re identification"
    - "occluded person re identification"
    - "large scale learnable graph convolutional networks"
    - "deformable convnets v2 more deformable better results"
    - "graph based global reasoning networks"
    - "random erasing data augmentation"

Connected through hyperedge HE-6 (induced by source paper "single camera training for person re identification"):
  with members:
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "part aligned bilinear representations for person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-7 (induced by source paper "discovering underlying person structure pattern with relative local distance for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "learning view specific deep networks for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "deep mutual learning"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "re ranking person re identification with k reciprocal encoding"
    - "glad global local alignment descriptor for pedestrian retrieval"

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)