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: "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"
  Abstract: Vacuum-based end effectors are widely used in industry and are often preferred over parallel-jaw and multifinger grippers due to their ability to lift objects with a single point of contact. Suction grasp planners often target planar surfaces on point clouds near the estimated centroid of an object. In this paper, we propose a compliant suction contact model that computes the quality of the seal between the suction cup and local target surface and a measure of the ability of the suction grasp to resist an external gravity wrench. To characterize grasps, we estimate robustness to perturbations in end-effector and object pose, material properties, and external wrenches. We analyze grasps across 1,500 3D object models to generate Dex-Net 3.0, a dataset of 2.8 million point clouds, suction grasps, and grasp robustness labels. We use Dex-Net 3.0 to train a Grasp Quality Convolutional Neural Network (GQ-CNN) to classify robust suction targets in point clouds containing a single object. We evaluate the resulting system in 350 physical trials on an ABB YuMi fitted with a pneumatic suction gripper. When evaluated on novel objects that we categorize as Basic (prismatic or cylindrical), Typical (more complex geometry), and Adversarial (with few available suction-grasp points) Dex-Net 3.0 achieves success rates of 98$\%$, 82$\%$, and 58$\%$ respectively, improving to 81$\%$ in the latter case when the training set includes only adversarial objects. Code, datasets, and supplemental material can be found at this http URL .

Connected through hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "investigating the importance of shape features color constancy color spaces and similarity measures in open ended 3d object recognition"
    - "accelerating reinforcement learning for reaching using continuous curriculum learning"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "learning better generative models for dexterous single view grasping of novel objects"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "the sound of motions"
    - "co separating sounds of visual objects"

Connected through hyperedge HE-2 (induced by source paper "optimizing correlated graspability score and grasp regression for better grasp prediction"):
  with members:
    - "fully convolutional grasp detection network with oriented anchor box"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "robotic grasp detection using deep convolutional neural networks"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "online self supervised learning for object picking detecting optimum grasping position using a metric learning approach"):
  with members:
    - "grasp2vec learning object representations from self supervised grasping"
    - "zigzag learning for weakly supervised object detection"
    - "wavenet a generative model for raw audio"
    - "facenet a unified embedding for face recognition and clustering"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"

Connected through hyperedge HE-4 (induced by source paper "solving rubik s cube with a robot hand"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "active domain randomization"
    - "meta reinforcement learning with task embedding and shared policy"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "distilling policy distillation"
    - "deep dynamics models for learning dexterous manipulation"
    - "meta reinforcement learning as task inference"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-5 (induced by source paper "a survey of knowledge representation in service robotics"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "the limits and potentials of deep learning for robotics"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "blitznet a real time deep network for scene understanding"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "affordancenet an end to end deep learning approach for object affordance detection"

Connected through hyperedge HE-6 (induced by source paper "quickly inserting pegs into uncertain holes using multi view images and deep network trained on synthetic data"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "a probabilistic framework for tracking uncertainties in robotic manipulation"
    - "learning robotic assembly from cad"
    - "learning dexterous in hand manipulation"
    - "deep learning scooping motion using bilateral teleoperations"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "experimental force torque dataset for robot learning of multi shape insertion"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"

Connected through hyperedge HE-7 (induced by source paper "directional semantic grasping of real world objects from simulation to reality"):
  with members:
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "unity a general platform for intelligent agents"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "adversarial discriminative sim to real transfer of visuo motor policies"
    - "multi task domain adaptation for deep learning of instance grasping from simulation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-8 (induced by source paper "a single multi task deep neural network with post processing for object detection with reasoning and robotic grasp detection"):
  with members:
    - "roi based robotic grasp detection for object overlapping scenes"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "yolov3 an incremental improvement"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"
    - "focal loss for dense object detection"
    - "robotic grasp detection using deep convolutional neural networks"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"

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)