human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "shape completion enabled robotic grasping":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "fusionnet 3d object classification using multiple data representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "indoor scene understanding in 2 5 3d for autonomous agents a survey")
  Member "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "gift a real time and scalable 3d shape search engine":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "deep sliding shapes for amodal 3d object detection in rgb d images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "shape and symmetry induction for 3d objects":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pose estimation for objects with rotational symmetry")
  Member "convolutional models for joint object categorization and pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unified perceptual parsing for scene understanding")
  Member "3d shapenets a deep representation for volumetric shapes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

Question: Please tell me which class the source paper 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)