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: "active vision for dexterous grasping of novel objects"
  Abstract: How should a robot direct active vision so as to ensure reliable grasping? We answer this question for the case of dexterous grasping of unfamiliar objects. By dexterous grasping we simply mean grasping by any hand with more than two fingers, such that the robot has some choice about where to place each finger. Such grasps typically fail in one of two ways, either unmodeled objects in the scene cause collisions or object reconstruction is insufficient to ensure that the grasp points provide a stable force closure. These problems can be solved more easily if active sensing is guided by the anticipated actions. Our approach has three stages. First, we take a single view and generate candidate grasps from the resulting partial object reconstruction. Second, we drive the active vision approach to maximise surface reconstruction quality around the planned contact points. During this phase, the anticipated grasp is continually refined. Third, we direct gaze to improve the safety of the planned reach to grasp trajectory. We show, on a dexterous manipulator with a camera on the wrist, that our approach (80.4% success rate) outperforms a randomised algorithm (64.3% success rate).

Connected through hyperedge HE-1 (induced by source paper "deep robotic prediction with hierarchical rgb d fusion"):
  with members:
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "learning robotic assembly from cad"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "learning to represent haptic feedback for partially observable tasks"
    - "grasp pose detection in point clouds"

Connected through hyperedge HE-2 (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"
    - "modeling grasp motor imagery through deep conditional generative models"
    - "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-3 (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"
    - "modeling grasp motor imagery through deep conditional generative models"
    - "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-4 (induced by source paper "2 5d image based robotic grasping"):
  with members:
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "learning robotic assembly from cad"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "grasp pose detection in point clouds"
    - "sim2real view invariant visual servoing by recurrent control"

Connected through hyperedge HE-5 (induced by source paper "multi view picking next best view reaching for improved grasping in clutter"):
  with members:
    - "grasp pose detection in point clouds"
    - "robotic grasp detection using deep convolutional neural networks"
    - "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"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-6 (induced by source paper "learning a visuomotor controller for real world robotic grasping using easily simulated depth images"):
  with members:
    - "high precision grasp pose detection in dense clutter"
    - "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"
    - "end to end training of deep visuomotor policies"
    - "real time grasp detection using convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-7 (induced by source paper "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"):
  with members:
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "continuous control with deep reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "end to end training of deep visuomotor policies"
    - "real time grasp detection using convolutional neural networks"

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)