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: "generating grasp poses for a high dof gripper using neural networks"
  Abstract: We present a learning-based method to represent grasp poses of a high-DOF hand using neural networks. Due to the redundancy in such high-DOF grippers, there exists a large number of equally effective grasp poses for a given target object, making it difficult for the neural network to find consistent grasp poses. In other words, it is difficult to find grasp poses for many objects that can be represented by a single neural network. We resolve this ambiguity by generating an augmented dataset that covers many possible grasps for each target object and train our neural networks using a consistency loss function to identify a one-to-one mapping from objects to grasp poses. We further enhance the quality of neuralnetwork-predicted grasp poses using an additional collision loss function to avoid penetrations. We show that our method can generate high-DOF grasp poses with higher accuracy than supervised learning baselines. The quality of grasp poses are on par with the groundtruth poses in the dataset. In addition, our method is robust and can handle imperfect or inaccurate object models, such as those constructed from multi-view depth images, allowing our method to be implemented on a 25-DOF Shadow Hand hardware platform.

Connected through hyperedge HE-1 (induced by source paper "deep differentiable grasp planner for high dof grippers"):
  with members:
    - "new formulation of mixed integer conic programming for globally optimal grasp planning"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "difftaichi differentiable programming for physical simulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "chainqueen a real time differentiable physical simulator for soft robotics"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "gomp grasp optimized motion planning for bin picking"):
  with members:
    - "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"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"

Connected through hyperedge HE-4 (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"
    - "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:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "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:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "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"

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)