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: "modeling grasp motor imagery through deep conditional generative models"
  Abstract: Grasping is a complex process involving knowledge of the object, the surroundings, and of oneself. While humans are able to integrate and process all of the sensory information required for performing this task, equipping machines with this capability is an extremely challenging endeavor. In this paper, we investigate how deep learning techniques can allow us to translate high-level concepts such as motor imagery to the problem of robotic grasp synthesis. We explore a paradigm based on generative models for learning integrated object-action representations, and demonstrate its capacity for capturing and generating multimodal, multi-finger grasp configurations on a simulated grasping dataset.

Connected through hyperedge HE-1 (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"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "robotic grasp detection using deep convolutional neural networks"
    - "high precision grasp pose detection in dense clutter"

Connected through hyperedge HE-2 (induced by source paper "planning multi fingered grasps as probabilistic inference in a learned deep network"):
  with members:
    - "robotic grasp detection using deep convolutional neural networks"
    - "high precision grasp pose detection in dense clutter"
    - "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-3 (induced by source paper "learning continuous 3d reconstructions for geometrically aware grasping"):
  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"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "robust grasp planning over uncertain shape completions"
    - "modeling grasp type improves learning based grasp planning"
    - "robust learning of tactile force estimation through robot interaction"
    - "learning implicit fields for generative shape modeling"
    - "pointconv deep convolutional networks on 3d point clouds"

Connected through hyperedge HE-4 (induced by source paper "deep dexterous grasping of novel objects from a single view"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "learning better generative models for dexterous single view grasping of novel objects"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "active vision for dexterous grasping of novel objects"
    - "robotic grasp detection using deep convolutional neural networks"
    - "high precision grasp pose detection in dense clutter"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "learning better generative models for dexterous single view grasping of novel objects"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "modeling grasp type improves learning based grasp planning"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "active vision for dexterous grasping of novel objects"
    - "robotic grasp detection using deep convolutional neural networks"
    - "high precision grasp pose detection in dense clutter"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"

Connected through hyperedge HE-6 (induced by source paper "unigrasp learning a unified model to grasp with n fingered robotic hands"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "tf ranking scalable tensorflow library for learning to rank"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning representations and generative models for 3d point clouds"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "a point set generation network for 3d object reconstruction from a single image"

Connected through hyperedge HE-7 (induced by source paper "modeling grasp type improves learning based grasp planning"):
  with members:
    - "robotic grasp detection using deep convolutional neural networks"
    - "high precision grasp pose detection in dense clutter"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "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-8 (induced by source paper "an integrated simulator and dataset that combines grasping and vision for deep learning"):
  with members:
    - "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"
    - "tools for dynamics simulation of robots a survey based on user feedback"
    - "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)