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: "learning robotic assembly from cad"
  Abstract: In this work, motivated by recent manufacturing trends, we investigate autonomous robotic assembly. Industrial assembly tasks require contact-rich manipulation skills, which are challenging to acquire using classical control and motion planning approaches. Consequently, robot controllers for assembly domains are presently engineered to solve a particular task, and cannot easily handle variations in the product or environment. Reinforcement learning (RL) is a promising approach for autonomously acquiring robot skills that involve contact-rich dynamics. However, RL relies on random exploration for learning a control policy, which requires many robot executions, and often gets trapped in locally suboptimal solutions. Instead, we posit that prior knowledge, when available, can improve RL performance. We exploit the fact that in modern assembly domains, geometric information about the task is readily available via the CAD design files. We propose to leverage this prior knowledge by guiding RL along a geometric motion plan, calculated using the CAD data. We show that our approach effectively improves over traditional control approaches for tracking the motion plan, and can solve assembly tasks that require high precision, even without accurate state estimation. In addition, we propose a neural network architecture that can learn to track the motion plan, and generalize the assembly controller to changes in the object positions.

Connected through hyperedge HE-1 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras"):
  with members:
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "grip generative robust inference and perception for semantic robot manipulation in adversarial environments"
    - "learning pose estimation for high precision robotic assembly using simulated depth images"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-2 (induced by source paper "learning contact rich manipulation tasks with rigid position controlled robots learning to force control"):
  with members:
    - "learning variable impedance control for contact sensitive tasks"
    - "variable impedance control in end effector space an action space for reinforcement learning in contact rich tasks"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "a framework for robot manipulation skill formalism meta learning and adaptive control"
    - "residual reinforcement learning for robot control"

Connected through hyperedge HE-3 (induced by source paper "efficient exploration in constrained environments with goal oriented reference path"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "learning navigation behaviors end to end with autorl"
    - "state representation learning for control an overview"
    - "motion planning networks"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deeply informed neural sampling for robot motion planning"

Connected through hyperedge HE-4 (induced by source paper "learning robotic manipulation through visual planning and acting"):
  with members:
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "model ensemble trust region policy optimization"
    - "visual reinforcement learning with imagined goals"
    - "learning plannable representations with causal infogan"
    - "spectral normalization for generative adversarial networks"
    - "bias and generalization in deep generative models an empirical study"
    - "visual robot task planning"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"):
  with members:
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "temporal difference models model free deep rl for model based control"
    - "residual policy learning"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "visual reinforcement learning with imagined goals"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "residual reinforcement learning for robot control"

Connected through hyperedge HE-6 (induced by source paper "composing diverse policies for temporally extended tasks"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "deepmdp learning continuous latent space models for representation learning"
    - "from explanation to synthesis compositional program induction for learning from demonstration"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "learning safe policies with expert guidance"

Connected through hyperedge HE-7 (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 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"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"

Connected through hyperedge HE-8 (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"
    - "modeling grasp type improves learning based grasp planning"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "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)