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: "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
  Abstract: Progress has been achieved recently in object detection given advancements in deep learning. Nevertheless, such tools typically require a large amount of training data and significant manual effort to label objects. This limits their applicability in robotics, where solutions must scale to a large number of objects and variety of conditions. This work proposes an autonomous process for training a Convolutional Neural Network (CNN) for object detection and pose estimation in robotic setups. The focus is on detecting objects placed in cluttered, tight environments, such as a shelf with multiple objects. In particular, given access to 3D object models, several aspects of the environment are physically simulated. The models are placed in physically realistic poses with respect to their environment to generate a labeled synthetic dataset. To further improve object detection, the network self-trains over real images that are labeled using a robust multi-view pose estimation process. The proposed training process is evaluated on several existing datasets and on a dataset collected for this paper with a Motoman robotic arm. Results show that the proposed approach outperforms popular training processes relying on synthetic - but not physically realistic - data and manual annotation. The key contributions are the incorporation of physical reasoning in the synthetic data generation process and the automation of the annotation process over real images.

Connected through hyperedge HE-1 (induced by source paper "template based unseen instance detection"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "deceptionnet network driven domain randomization"
    - "learning to track any object"
    - "on the convergence of adam and beyond"
    - "photorealistic image synthesis for object instance detection"
    - "target aware deep tracking"
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"

Connected through hyperedge HE-2 (induced by source paper "online object representations with contrastive learning"):
  with members:
    - "self supervisory signals for object discovery and detection"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "superpoint self supervised interest point detection and description"
    - "object category learning and retrieval with weak supervision"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "deep image prior"
    - "mixup beyond empirical risk minimization"
    - "time contrastive networks self supervised learning from video"

Connected through hyperedge HE-3 (induced by source paper "an annotation saved is an annotation earned using fully synthetic training for object instance detection"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "applying domain randomization to synthetic data for object category detection"
    - "transfer learning from synthetic to real images using variational autoencoders for precise position detection"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (induced by source paper "implicit 3d orientation learning for 6d object detection from rgb images"):
  with members:
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "6d pose estimation using an improved method based on point pair features"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "3d pose regression using convolutional neural networks"
    - "domain adaptation for visual applications a comprehensive survey"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (induced by source paper "self supervised 6d object pose estimation for robot manipulation"):
  with members:
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "a billion ways to grasp an evaluation of grasp sampling schemes on a dense physics based grasp data set"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "learning instance segmentation by interaction"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "synthetic data generation and adaption for object detection in smart vending machines"):
  with members:
    - "specular to diffuse translation for multi view reconstruction"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "object detection using deep cnns trained on synthetic images"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-7 (induced by source paper "semi automatic labeling for deep learning in robotics"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "focal loss for dense object detection"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "adversarial discriminative domain adaptation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-8 (induced by source paper "towards robust product packing with a minimalistic end effector"):
  with members:
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "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"

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.RO(Robotics)