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: "part based weighting aggregation of deep convolutional features for image retrieval"
  Abstract: Several recent works have shown that part-based image representation provides state-of-the-art performance for fine-grained categorization. Moreover, it has also been shown that image global representation generated by aggregating deep convolutional features provides excellent performance for image retrieval. In this paper we propose a novel aggregation method, which utilizes the information of retrieval object parts. The proposed part-based weighting aggregation (PWA) method utilizes the normalized feature maps as part detectors to weight and aggregate the convolutional features. The part detectors which are selected by the unsupervised method highlight the discriminative parts of objects and effectively suppress the noise of background. We experiment on five public standard datasets for image retrieval. Our unsupervised PWA method outperforms the state-of-the-art approaches based on pre-trained networks and achieves comparable accuracy with the fine-tuned methods. It is worth noting that our unsupervised method is very suitable and effective for the situation where the annotated training dataset is difficult to collect.

Connected through hyperedge HE-1 (induced by source paper "multi feature fusion for image retrieval using constrained dominant sets"):
  with members:
    - "deep constrained dominant sets for person re identification"
    - "deep group shuffling random walk for person re identification"
    - "dominant sets for constrained image segmentation"
    - "deep image retrieval learning global representations for image search"
    - "interactive image segmentation using constrained dominant sets"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "cross dimensional weighting for aggregated deep convolutional features"

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)