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: "high precision grasp pose detection in dense clutter"
  Abstract: This paper considers the problem of grasp pose detection in point clouds. We follow a general algorithmic structure that first generates a large set of 6-DOF grasp candidates and then classifies each of them as a good or a bad grasp. Our focus in this paper is on improving the second step by using depth sensor scans from large online datasets to train a convolutional neural network. We propose two new representations of grasp candidates, and we quantify the effect of using prior knowledge of two forms: instance or category knowledge of the object to be grasped, and pretraining the network on simulated depth data obtained from idealized CAD models. Our analysis shows that a more informative grasp candidate representation as well as pretraining and prior knowledge significantly improve grasp detection. We evaluate our approach on a Baxter Research Robot and demonstrate an average grasp success rate of 93% in dense clutter. This is a 20% improvement compared to our prior work.

Connected through hyperedge HE-1 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds"):
  with members:
    - "object detection in 20 years a survey"
    - "relation shape convolutional neural network for point cloud analysis"
    - "roi based robotic grasp detection for object overlapping scenes"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "a real time robotic grasp approach with oriented anchor box"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-2 (induced by source paper "learning to singulate objects using a push proposal network"):
  with members:
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "metric learning for generalizing spatial relations to new objects"
    - "interactive perception leveraging action in perception and perception in action"
    - "fast model identification via physics engines for data efficient policy search"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "comparing human centric and robot centric sampling for robot deep learning from demonstrations"
    - "deep visual foresight for planning robot motion"
    - "se3 nets learning rigid body motion using deep neural networks"

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 "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"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "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-5 (induced by source paper "domain independent unsupervised learning to grasp the novel objects"):
  with members:
    - "learning a visuomotor controller for real world robotic grasping using easily simulated depth images"
    - "robotic grasp detection using deep convolutional neural networks"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "using geometry to detect grasps in 3d point clouds"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "real time grasp pose estimation for novel objects in densely cluttered environment"):
  with members:
    - "pick and place without geometric object models"
    - "grasp pose detection in point clouds"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "data efficient learning for sim to real robotic grasping using deep point cloud prediction networks"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "grasp2vec learning object representations from self supervised grasping"
    - "3d shape perception from monocular vision touch and shape priors"
    - "multiresolution tree networks for 3d point cloud processing"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"

Connected through hyperedge HE-8 (induced by source paper "learning one shot imitation from humans without humans"):
  with members:
    - "pyrep bringing v rep to deep robot learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "deep episodic memory encoding recalling and predicting episodic experiences for robot action execution"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "task embedded control networks for few shot imitation learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "one shot visual imitation learning via meta learning"

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)