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: "hybrid bayesian eigenobjects combining linear subspace and deep network methods for 3d robot vision"
  Abstract: We introduce Hybrid Bayesian Eigenobjects (HBEOs), a novel representation for 3D objects designed to allow a robot to jointly estimate the pose, class, and full 3D geometry of a novel object observed from a single viewpoint in a single practical framework. By combining both linear subspace methods and deep convolutional prediction, HBEOs efficiently learn nonlinear object representations without directly regressing into high-dimensional space. HBEOs also remove the onerous and generally impractical necessity of input data voxelization prior to inference. We experimentally evaluate the suitability of HBEOs to the challenging task of joint pose, class, and shape inference on novel objects and show that, compared to preceding work, HBEOs offer dramatically improved performance in all three tasks along with several orders of magnitude faster runtime performance.

Connected through hyperedge HE-1 (induced by source paper "deep learning vs traditional computer vision"):
  with members:
    - "recurrent convolutional fusion for rgb d object recognition"
    - "hybrid scene compression for visual localization"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "significance of softmax based features in comparison to distance metric learning based features"

Connected through hyperedge HE-2 (induced by source paper "grounding language attributes to objects using bayesian eigenobjects"):
  with members:
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "text2shape generating shapes from natural language by learning joint embeddings"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "shapenet an information rich 3d model repository"
    - "towards universal paraphrastic sentence embeddings"
    - "natural language object retrieval"
    - "3d shapenets a deep representation for volumetric shapes"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "probabilistic category level pose estimation via segmentation and predicted shape priors"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "mask r cnn"
    - "shapenet an information rich 3d model repository"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "viewpoints and keypoints"

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)