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 type improves learning based grasp planning"
  Abstract: Different manipulation tasks require different types of grasps. For example, holding a heavy tool like a hammer requires a multi-fingered power grasp offering stability, while holding a pen to write requires a multi-fingered precision grasp to impart dexterity on the object. In this paper, we propose a probabilistic grasp planner that explicitly models grasp type for planning high-quality precision and power grasps in real-time. We take a learning approach in order to plan grasps of different types for previously unseen objects when only partial visual information is available. Our work demonstrates the first supervised learning approach to grasp planning that can explicitly plan both power and precision grasps for a given object. Additionally, we compare our learned grasp model with a model that does not encode type and show that modeling grasp type improves the success rate of generated grasps. Furthermore we show the benefit of learning a prior over grasp configurations to improve grasp inference with a learned classifier.

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"
    - "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"
    - "high precision grasp pose detection in dense clutter"

Connected through hyperedge HE-2 (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"
    - "robust learning of tactile force estimation through robot interaction"
    - "learning implicit fields for generative shape modeling"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"

Connected through hyperedge HE-3 (induced by source paper "contextual reinforcement learning of visuo tactile multi fingered grasping policies"):
  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"
    - "sim to real transfer for biped locomotion"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "learning robotic assembly from cad"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-4 (induced by source paper "mat multi fingered adaptive tactile grasping via deep reinforcement learning"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "3d shape perception from monocular vision touch and shape priors"
    - "learning to grasp without seeing"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "leveraging contact forces for learning to grasp"
    - "soft actor critic algorithms and applications"

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"
    - "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"
    - "modeling grasp motor imagery through deep conditional generative models"
    - "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 "generating grasp poses for a high dof gripper using neural networks"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "learning dexterous in hand manipulation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

Connected through hyperedge HE-7 (induced by source paper "state classification of cooking objects using a vgg cnn"):
  with members:
    - "identifying object states in cooking related images"

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)