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: "robust 6d object pose estimation with stochastic congruent sets"
  Abstract: Object pose estimation is frequently achieved by first segmenting an RGB image and then, given depth data, registering the corresponding point cloud segment against the object's 3D model. Despite the progress due to CNNs, semantic segmentation output can be noisy, especially when the CNN is only trained on synthetic data. This causes registration methods to fail in estimating a good object pose. This work proposes a novel stochastic optimization process that treats the segmentation output of CNNs as a confidence probability. The algorithm, called Stochastic Congruent Sets (StoCS), samples pointsets on the point cloud according to the soft segmentation distribution and so as to agree with the object's known geometry. The pointsets are then matched to congruent sets on the 3D object model to generate pose estimates. StoCS is shown to be robust on an APC dataset, despite the fact the CNN is trained only on synthetic data. In the YCB dataset, StoCS outperforms a recent network for 6D pose estimation and alternative pointset matching techniques.

Connected through hyperedge HE-1 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands"):
  with members:
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "scene level pose estimation for multiple instances of densely packed objects"
    - "learning joint reconstruction of hands and manipulated objects"
    - "manipulation by feel touch based control with deep predictive models"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "principal components of touch"

Connected through hyperedge HE-2 (induced by source paper "lit light field inference of transparency for refractive object localization"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "glassloc plenoptic grasp pose detection in transparent clutter"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "grip generative robust inference and perception for semantic robot manipulation in adversarial environments"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "segmentation driven 6d object pose estimation"
    - "plenoptic monte carlo object localization for robot grasping under layered translucency"

Connected through hyperedge HE-3 (induced by source paper "grip generative robust inference and perception for semantic robot manipulation in adversarial environments"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "robust physical world attacks on deep learning models"
    - "pick and place without geometric object models"
    - "sum sequential scene understanding and manipulation"
    - "grasp pose detection in point clouds"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "semantic robot programming for goal directed manipulation in cluttered scenes"

Connected through hyperedge HE-4 (induced by source paper "towards robust product packing with a minimalistic end effector"):
  with members:
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "improving 6d pose estimation of objects in clutter via physics aware monte carlo tree search"
    - "mask r cnn"
    - "analysis and observations from the first amazon picking challenge"
    - "global hypothesis generation for 6d object pose estimation"

Connected through hyperedge HE-5 (induced by source paper "multi view shape estimation of transparent containers"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "mask r cnn"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-6 (induced by source paper "scene level pose estimation for multiple instances of densely packed objects"):
  with members:
    - "global hypothesis generation for 6d object pose estimation"
    - "going further with point pair features"

Connected through hyperedge HE-7 (induced by source paper "physics based scene level reasoning for object pose estimation in clutter"):
  with members:
    - "fusion volumetric object level slam"
    - "bop benchmark for 6d object pose estimation"
    - "6d pose estimation using an improved method based on point pair features"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "rotational subgroup voting and pose clustering for robust 3d object recognition"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "genesis rt generating synthetic images for training secondary real world tasks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "learning object localization and 6d pose estimation from simulation and weakly labeled real images"):
  with members:
    - "bop benchmark for 6d object pose estimation"
    - "deep visual domain adaptation a survey"
    - "multi adversarial domain adaptation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "6d pose estimation using an improved method based on point pair features"
    - "domain adaptation for visual applications a comprehensive survey"

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)