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: "instance and category level 6d object pose estimation"
  Abstract: Interest in estimating the 6D pose, i.e. 3D locations and rotations, of an object of interest has emerged since its promising applications in fields such as robotics and augmented reality. To recover poses from objects that have been seen in advance, instance-level methods have been presented to overcome challenges such as occlusion, clutter and similarly looking distractors. The problem has recently been addressed at the category level, where poses of object instances from a given category that have not been seen a priori are estimated, introducing new challenges such as distribution shifts and intra-class variations. In this chapter, the 6D object pose estimation problem at the levels of both instances and categories is presented, discussed, and analysed by following the available literature on the topic. First, the problem and its associated challenges are formulated and presented. To continue, instance-level methods are dissected depending on their architectures and category-level methods are examined according to their search space dimension. Popular datasets, benchmarks and evaluation metrics on the problem are presented and studied with respect to the challenges that they present. Quantitative results of experiments available in the literature are analysed to determine how methods perform when presented with different challenges. The analyses are further extended to compare three methods by using our own implementations aiming to solidify already published results. To conclude, the current state of the field is summarised and potential future research directions are identified.

Connected through hyperedge HE-1 (induced by source paper "learning canonical shape space for category level 6d object pose and size estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "6 pack category level 6d pose tracker with anchor based keypoints"
    - "real time 6d object pose estimation on cpu"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "category level 6d object pose recovery in depth images"
    - "group normalization"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "matching rgb images to cad models for object pose estimation"

Connected through hyperedge HE-2 (induced by source paper "active 6d multi object pose estimation in cluttered scenarios with deep reinforcement learning"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "bop benchmark for 6d object pose estimation"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "deep object centric policies for autonomous driving"

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)