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: "team delft s robot winner of the amazon picking challenge 2016"
  Abstract: This paper describes Team Delft's robot, which won the Amazon Picking Challenge 2016, including both the Picking and the Stowing competitions. The goal of the challenge is to automate pick and place operations in unstructured environments, specifically the shelves in an Amazon warehouse. Team Delft's robot is based on an industrial robot arm, 3D cameras and a customized gripper. The robot's software uses ROS to integrate off-the-shelf components and modules developed specifically for the competition, implementing Deep Learning and other AI techniques for object recognition and pose estimation, grasp planning and motion planning. This paper describes the main components in the system, and discusses its performance and results at the Amazon Picking Challenge 2016 finals.

Connected through hyperedge HE-1 (induced by source paper "instance segmentation of visible and occluded regions for finding and picking target from a pile of objects"):
  with members:
    - "probabilistic 3d multilabel real time mapping for multi object manipulation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "on pre trained image features and synthetic images for deep learning"
    - "synthesizing training data for object detection in indoor scenes"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "learning from simulated and unsupervised images through adversarial training"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"

Connected through hyperedge HE-2 (induced by source paper "development of a robotic system for automated decaking of 3d printed parts"):
  with members:
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "a summary of team mit s approach to the amazon picking challenge 2015"
    - "analysis and observations from the first amazon picking challenge"
    - "lessons from the amazon picking challenge"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (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"
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "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"

Connected through hyperedge HE-4 (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"
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "a summary of the 4th international workshop on recovering 6d object pose"):
  with members:
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"
    - "3d coded 3d correspondences by deep deformation"
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "fully convolutional point networks for large scale point clouds"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-6 (induced by source paper "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"):
  with members:
    - "nimbro rescue solving disaster response tasks through mobile manipulation robot momaro"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "a summary of team mit s approach to the amazon picking challenge 2015"
    - "iterative visual recognition for learning based randomized bin picking"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "densecap fully convolutional localization networks for dense captioning"

Connected through hyperedge HE-7 (induced by source paper "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"):
  with members:
    - "uncertainty in multitask learning joint representations for probabilistic mr only radiotherapy planning"
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "bayesian image quality transfer with cnns exploring uncertainty in dmri super resolution"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deep bayesian active learning with image data"
    - "propagating uncertainty in multi stage bayesian convolutional neural networks with application to pulmonary nodule detection"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "improved gq cnn deep learning model for planning robust grasps"):
  with members:
    - "on calibration of modern neural networks"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)