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: "deep learning a grasp function for grasping under gripper pose uncertainty"
  Abstract: This paper presents a new method for parallel-jaw grasping of isolated objects from depth images, under large gripper pose uncertainty. Whilst most approaches aim to predict the single best grasp pose from an image, our method first predicts a score for every possible grasp pose, which we denote the grasp function. With this, it is possible to achieve grasping robust to the gripper's pose uncertainty, by smoothing the grasp function with the pose uncertainty function. Therefore, if the single best pose is adjacent to a region of poor grasp quality, that pose will no longer be chosen, and instead a pose will be chosen which is surrounded by a region of high grasp quality. To learn this function, we train a Convolutional Neural Network which takes as input a single depth image of an object, and outputs a score for each grasp pose across the image. Training data for this is generated by use of physics simulation and depth image simulation with 3D object meshes, to enable acquisition of sufficient data without requiring exhaustive real-world experiments. We evaluate with both synthetic and real experiments, and show that the learned grasp score is more robust to gripper pose uncertainty than when this uncertainty is not accounted for.

Connected through hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "investigating the importance of shape features color constancy color spaces and similarity measures in open ended 3d object recognition"
    - "accelerating reinforcement learning for reaching using continuous curriculum learning"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "learning better generative models for dexterous single view grasping of novel objects"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "the sound of motions"
    - "co separating sounds of visual objects"

Connected through hyperedge HE-2 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping"):
  with members:
    - "directional semantic grasping of real world objects from simulation to reality"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "metagrasp data efficient grasping by affordance interpreter network"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"

Connected through hyperedge HE-3 (induced by source paper "multi fingered grasp planning via inference in deep neural networks"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "generating grasp poses for a high dof gripper using neural networks"
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "modeling grasp type improves learning based grasp planning"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "modeling grasp motor imagery through deep conditional generative models"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "robotic grasp detection using deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "egad an evolved grasping analysis dataset for diversity and reproducibility in robotic manipulation"):
  with members:
    - "pointnetgpd detecting grasp configurations from point sets"
    - "towards the targeted environment specific evolution of robot components"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "diversity is all you need learning skills without a reward function"
    - "densely supervised grasp detector dsgd"
    - "multi view picking next best view reaching for improved grasping in clutter"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-5 (induced by source paper "gomp grasp optimized motion planning for bin picking"):
  with members:
    - "generating grasp poses for a high dof gripper using neural networks"
    - "manipulation trajectory optimization with online grasp synthesis and selection"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "a billion ways to grasp an evaluation of grasp sampling schemes on a dense physics based grasp data set"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "grasp pose detection in point clouds"

Connected through hyperedge HE-6 (induced by source paper "planning multi fingered grasps as probabilistic inference in a learned deep network"):
  with members:
    - "modeling grasp motor imagery through deep conditional generative models"
    - "robotic grasp detection using deep convolutional neural networks"
    - "high precision grasp pose detection in dense clutter"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "real time grasp detection using convolutional neural networks"
    - "data driven grasp synthesis a survey"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-7 (induced by source paper "control of the final phase of closed loop visual grasping using image based visual servoing"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "semi supervised deep learning for monocular depth map prediction"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-8 (induced by source paper "predicting target feature configuration of non stationary objects for grasping with image based visual servoing"):
  with members:
    - "deep learning for detecting robotic grasps"

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)