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: "segmenting unknown 3d objects from real depth images using mask r cnn trained on synthetic data"
  Abstract: The ability to segment unknown objects in depth images has potential to enhance robot skills in grasping and object tracking. Recent computer vision research has demonstrated that Mask R-CNN can be trained to segment specific categories of objects in RGB images when massive hand-labeled datasets are available. As generating these datasets is time consuming, we instead train with synthetic depth images. Many robots now use depth sensors, and recent results suggest training on synthetic depth data can transfer successfully to the real world. We present a method for automated dataset generation and rapidly generate a synthetic training dataset of 50,000 depth images and 320,000 object masks using simulated heaps of 3D CAD models. We train a variant of Mask R-CNN with domain randomization on the generated dataset to perform category-agnostic instance segmentation without any hand-labeled data and we evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution depth images of challenging, densely-cluttered bins containing objects with highly-varied geometry. SD Mask R-CNN outperforms point cloud clustering baselines by an absolute 15% in Average Precision and 20% in Average Recall on COCO benchmarks, and achieves performance levels similar to a Mask R-CNN trained on a massive, hand-labeled RGB dataset and fine-tuned on real images from the experimental setup. We deploy the model in an instance-specific grasping pipeline to demonstrate its usefulness in a robotics application. Code, the synthetic training dataset, and supplementary material are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "segmenting unseen industrial components in a heavy clutter using rgb d fusion and synthetic data"):
  with members:
    - "large scale 6d object pose estimation dataset for industrial bin picking"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "photorealistic image synthesis for object instance detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "mask r cnn"
    - "feature pyramid networks for object detection"
    - "learning descriptors for object recognition and 3d pose estimation"

Connected through hyperedge HE-2 (induced by source paper "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "easylabel a semi automatic pixel wise object annotation tool for creating robotic rgb d datasets"
    - "towards segmenting everything that moves"
    - "large scale interactive object segmentation with human annotators"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "shapemask learning to segment novel objects by refining shape priors"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "group normalization"

Connected through hyperedge HE-3 (induced by source paper "mechanical search multi step retrieval of a target object occluded by clutter"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "one shot segmentation in clutter"
    - "visual representations for semantic target driven navigation"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "interactive perception leveraging action in perception and perception in action"
    - "neural task programming learning to generalize across hierarchical tasks"

Connected through hyperedge HE-4 (induced by source paper "using synthetic data and deep networks to recognize primitive shapes for object grasping"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "classification based grasp detection using spatial transformer network"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "beyond top grasps through scene completion"):
  with members:
    - "deep dexterous grasping of novel objects from a single view"
    - "robust grasp planning over uncertain shape completions"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "meshnet mesh neural network for 3d shape representation"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "multi modal geometric learning for grasping and manipulation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"

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)