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 self paced learning for person re identification"
  Abstract: Person re-identification (Re-ID) usually suffers from noisy samples with background clutter and mutual occlusion, which makes it extremely difficult to distinguish different individuals across the disjoint camera views. In this paper, we propose a novel deep self-paced learning (DSPL) algorithm to alleviate this problem, in which we apply a self-paced constraint and symmetric regularization to help the relative distance metric training the deep neural network, so as to learn the stable and discriminative features for person Re-ID. Firstly, we propose a soft polynomial regularizer term which can derive the adaptive weights to samples based on both the training loss and model age. As a result, the high-confidence fidelity samples will be emphasized and the low-confidence noisy samples will be suppressed at early stage of the whole training process. Such a learning regime is naturally implemented under a self-paced learning (SPL) framework, in which samples weights are adaptively updated based on both model age and sample loss using an alternative optimization method. Secondly, we introduce a symmetric regularizer term to revise the asymmetric gradient back-propagation derived by the relative distance metric, so as to simultaneously minimize the intra-class distance and maximize the inter-class distance in each triplet unit. Finally, we build a part-based deep neural network, in which the features of different body parts are first discriminately learned in the lower convolutional layers and then fused in the higher fully connected layers. Experiments on several benchmark datasets have demonstrated the superior performance of our method as compared with the state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "improving person re identification by attribute and identity learning"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "attention aware compositional network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "person search with natural language description"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "adversarial attribute image person re identification"

Connected through hyperedge HE-2 (induced by source paper "attention driven person re identification"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "deep representation learning with part loss for person re identification"
    - "person re identification with metric learning using privileged information"
    - "improving person re identification by attribute and identity learning"
    - "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"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-3 (induced by source paper "discriminative feature learning with foreground attention for person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "efficient and deep person re identification using multi level similarity"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "human semantic parsing for person re identification"
    - "resource aware person re identification across multiple resolutions"

Connected through hyperedge HE-4 (induced by source paper "semantically selective augmentation for deep compact person re identification"):
  with members:
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "pose guided person image generation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "condensenet an efficient densenet using learned group convolutions"
    - "hand keypoint detection in single images using multiview bootstrapping"

Connected through hyperedge HE-5 (induced by source paper "one pass person re identification by sketch online discriminant analysis"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "person re identification by camera correlation aware feature augmentation"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "adversarial attribute image person re identification"
    - "unsupervised adaptive re identification in open world dynamic camera networks"
    - "svdnet 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)