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: "discriminative feature learning with foreground attention for person re identification"
  Abstract: The performance of person re-identification (Re-ID) seriously depends on the camera environment, where the large cross-view appearance variations caused by mutual occlusions and background clutters have severely decreased the identification accuracy. Therefore, it is very essential to learn a feature representation that can adaptively emphasize the foreground of each input image. In this paper, we propose a simple yet effective deep neural network to solve the person Re-ID problem, which attempts to learn a discriminative feature representation by addressing the foreground of input images. Specifically, a novel foreground attentive neural network (FANN) is first built to strengthen the positive influence of foreground while weaken the side effect of background, in which an encoder and decoder subnetwork is carefully designed to drive the whole neural network to put its attention on the foreground of input images. Then, a novel symmetric triplet loss function is designed to enhance the feature learning capability by jointly minimizing the intra-class distance and maximizing the inter-class distance in each triplet unit. Training the deep neural network in an end-to-end fashion, a discriminative feature representation can be finally learned to find out the matched reference to the probe among various candidates in the gallery. Comprehensive experiments on several public benchmark datasets are conducted to evaluate the performance, which have shown clear improvements of our method as compared with the state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "multi task learning with coarse priors for robust part aware person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "cdpm convolutional deformable part models for person re identification"
    - "relation aware global attention"
    - "multi level factorisation net for person re identification"
    - "attention aware compositional network for person re identification"
    - "factorized distillation training holistic person re identification model by distilling an ensemble of partial reid models"
    - "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-2 (induced by source paper "attkgcn attribute knowledge graph convolutional network for person re identification"):
  with members:
    - "learning context graph for person search"
    - "aanet attribute attention network for person re identifications"
    - "multi label image recognition with graph convolutional networks"
    - "attributes aided part detection and refinement for person re identification"
    - "pedestrian re identification based on tree branch network with local and global learning"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"
    - "improving person re identification by attribute and identity learning"

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)