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: "defining the pose of any 3d rigid object and an associated distance"
  Abstract: The pose of a rigid object is usually regarded as a rigid transformation, described by a translation and a rotation. However, equating the pose space with the space of rigid transformations is in general abusive, as it does not account for objects with proper symmetries—which are common among man-made objects. In this article, we define pose as a distinguishable static state of an object, and equate a pose to a set of rigid transformations. Based solely on geometric considerations, we propose a frame-invariant metric on the space of possible poses, valid for any physical rigid object, and requiring no arbitrary tuning. This distance can be evaluated efficiently using a representation of poses within a Euclidean space of at most 12 dimensions depending on the object’s symmetries. This makes it possible to efficiently perform neighborhood queries such as radius searches or k-nearest neighbor searches within a large set of poses using off-the-shelf methods. Pose averaging considering this metric can similarly be performed easily, using a projection function from the Euclidean space onto the pose space. The practical value of those theoretical developments is illustrated with an application of pose estimation of instances of a 3D rigid object given an input depth map, via a Mean Shift procedure.

Connected through hyperedge HE-1 (induced by source paper "on object symmetries and 6d pose estimation from images"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "pose estimation for objects with rotational symmetry"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"

Connected through hyperedge HE-2 (induced by source paper "large scale 6d object pose estimation dataset for industrial bin picking"):
  with members:
    - "bop benchmark for 6d object pose estimation"
    - "learning dexterous in hand manipulation"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "symmetry aware evaluation of 3d object detection and pose estimation in scenes of many parts in bulk"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "rgbd datasets past present and future"

Connected through hyperedge HE-3 (induced by source paper "symmetry aware evaluation of 3d object detection and pose estimation in scenes of many parts in bulk"):
  with members:
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "rgbd datasets past present and future"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "hashmod a hashing method for scalable 3d object detection"
    - "recovering 6d object pose and predicting next best view in the crowd"

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)