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: "planning multi fingered grasps as probabilistic inference in a learned deep network"
  Abstract: We propose a novel approach to multi-fingered grasp planning leveraging learned deep neural network models. We train a convolutional neural network to predict grasp success as a function of both visual information of an object and grasp configuration. We can then formulate grasp planning as inferring the grasp configuration which maximizes the probability of grasp success. We efficiently perform this inference using a gradient-ascent optimization inside the neural network using the backpropagation algorithm. Our work is the first to directly plan high quality multi-fingered grasps in configuration space using a deep neural network without the need of an external planner. We validate our inference method performing both multi-finger and two-finger grasps on real robots. Our experimental results show that our planning method outperforms existing planning methods for neural networks; while offering several other benefits including being data-efficient in learning and fast enough to be deployed in real robotic applications.

Connected through hyperedge HE-1 (induced by source paper "multi fingered grasp planning via inference in deep neural networks"):
  with members:
    - "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"
    - "high precision grasp pose detection in dense clutter"

Connected through hyperedge HE-2 (induced by source paper "reaching grasping and re grasping learning multimode grasping skills"):
  with members:
    - "learning dexterous in hand manipulation"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "leveraging contact forces for learning to grasp"
    - "learning whole body motor skills for humanoids"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "proximal policy optimization algorithms"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "concrete problems in ai safety"

Connected through hyperedge HE-3 (induced by source paper "grasping in the wild learning 6dof closed loop grasping from low cost demonstrations"):
  with members:
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "form2fit learning shape priors for generalizable assembly from disassembly"
    - "cleargrasp 3d shape estimation of transparent objects for manipulation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "learning 6 dof grasping and pick place using attention focus"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "learning continuous 3d reconstructions for geometrically aware grasping"):
  with members:
    - "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"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"

Connected through hyperedge HE-5 (induced by source paper "6 dof graspnet variational grasp generation for object manipulation"):
  with members:
    - "generating grasp poses for a high dof gripper using neural networks"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"
    - "learning 6 dof grasping interaction with deep geometry aware 3d representations"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-6 (induced by source paper "contextual reinforcement learning of visuo tactile multi fingered grasping policies"):
  with members:
    - "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"
    - "modeling grasp type improves learning based grasp planning"
    - "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-7 (induced by source paper "6 dof grasping for target driven object manipulation in clutter"):
  with members:
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "grasping unknown objects in clutter by superquadric representation"

Connected through hyperedge HE-8 (induced by source paper "roi based robotic grasp detection for object overlapping scenes"):
  with members:
    - "visual manipulation relationship network"
    - "dealing with ambiguity in robotic grasping via multiple predictions"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "real world multi object multi grasp detection"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "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"

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)