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 is the best practice for cnns applied to visual instance retrieval"
  Abstract: Previous work has shown that feature maps of deep convolutional neural networks (CNNs)#N#can be interpreted as feature representation of a particular image region. Features aggregated from#N#these feature maps have been exploited for image retrieval tasks and achieved state-of-the-art performances in#N#recent years. The key to the success of such methods is the feature representation. However, the different#N#factors that impact the effectiveness of features are still not explored thoroughly. There are much less#N#discussion about the best combination of them.#N##N#The main contribution of our paper is the thorough evaluations of the various factors that affect the#N#discriminative ability of the features extracted from CNNs. Based on the evaluation results, we also identify #N#the best choices for different factors and propose a new multi-scale image feature representation method to #N#encode the image effectively. Finally, we show that the proposed method generalises well and outperforms #N#the state-of-the-art methods on four typical datasets used for visual instance retrieval.

Connected through hyperedge HE-1 (induced by source paper "multi path learning for object pose estimation across domains"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "3d pose estimation and 3d model retrieval for objects in the wild"

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)