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: "what and where to match deep spatially multiplicative integration networks for person re identification"
  Abstract: Matching pedestrians across disjoint camera views, known as person re-identification (re-id), is a challenging problem that is of importance to visual recognition and surveillance. Most existing methods exploit local regions with spatial manipulation to perform matching in local correspondences. However, they essentially extract fixed representations from pre-divided regions for each image and then perform matching based on these extracted representations. For models in this pipeline, local finer patterns that are crucial to distinguish positive pairs from negative ones cannot be captured, and thus making them underperformed. In this paper, we propose a novel deep multiplicative integration gating function, which answers the question of what-and-where to match for effective person re-id. To address what to match, our deep network emphasizes common local patterns by learning joint representations in a multiplicative way. The network comprises two Convolutional Neural Networks (CNNs) to extract convolutional activations, and generates relevant descriptors for pedestrian matching. This leads to flexible representations for pair-wise images. To address where to match, we combat the spatial misalignment by performing spatially recurrent pooling via a four-directional recurrent neural network to impose spatial dependency over all positions with respect to the entire image. The proposed network is designed to be end-to-end trainable to characterize local pairwise feature interactions in a spatially aligned manner. To demonstrate the superiority of our method, extensive experiments are conducted over three benchmark data sets: VIPeR, CUHK03 and Market-1501.

Connected through hyperedge HE-1 (induced by source paper "few shot deep adversarial learning for video based person re identification"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "crossing generative adversarial networks for cross view person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "person re identification via recurrent feature aggregation"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "unsupervised person re identification clustering and fine tuning"

Connected through hyperedge HE-2 (induced by source paper "attribute guided feature learning network for vehicle re identification"):
  with members:
    - "a dual path model with adaptive attention for vehicle re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "cross entropy adversarial view adaptation for 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"

Connected through hyperedge HE-3 (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"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "sem gan semantically consistent image to image translation"
    - "crossing generative adversarial networks for cross view person re identification"

Connected through hyperedge HE-4 (induced by source paper "3 d personvlad learning deep global representations for video based person reidentification"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "towards good practices on building effective cnn baseline model for person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "person re identification via recurrent feature aggregation"
    - "scalable person re identification on supervised smoothed manifold"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"

Connected through hyperedge HE-5 (induced by source paper "cross entropy adversarial view adaptation for person re identification"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "human semantic parsing for person re identification"
    - "few shot adversarial domain adaptation"
    - "person re identification by deep joint learning of multi loss classification"
    - "adversarial discriminative domain adaptation"
    - "unified deep supervised domain adaptation and generalization"

Connected through hyperedge HE-6 (induced by source paper "cycle consistent deep generative hashing for cross modal retrieval"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "deep semantic hashing with generative adversarial networks"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "zero shot sketch image hashing"
    - "cm gans cross modal generative adversarial networks for common representation learning"
    - "cycada cycle consistent adversarial domain adaptation"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "hashgan attention aware deep adversarial hashing for cross modal retrieval"

Connected through hyperedge HE-7 (induced by source paper "using context information to enhance simple question answering"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "freebase triples a methodology for processing the freebase data dumps"
    - "attention is all you need"
    - "semi supervised multitask learning for sequence labeling"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-8 (induced by source paper "interpretable and generalizable deep image matching with adaptive convolutions"):
  with members:
    - "learning discriminative features with multiple granularities for person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "unsupervised domain adaptive re identification theory and practice"
    - "adaptation and re identification network an unsupervised deep transfer learning approach to person re identification"
    - "domain adaptation through synthesis 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)