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: "modular deep q networks for sim to real transfer of visuo motor policies"
  Abstract: While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly. To increase the practicality of these techniques on real robots, we propose a modular deep reinforcement learning method capable of transferring models trained in simulation to a real-world robotic task. We introduce a bottleneck between perception and control, enabling the networks to be trained independently, but then merged and fine-tuned in an end-to-end manner to further improve hand-eye coordination. On a canonical, planar visually-guided robot reaching task a fine-tuned accuracy of 1.6 pixels is achieved, a significant improvement over naive transfer (17.5 pixels), showing the potential for more complicated and broader applications. Our method provides a technique for more efficient learning and transfer of visuo-motor policies for real robotic systems without relying entirely on large real-world robot datasets.

Connected through hyperedge HE-1 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer"):
  with members:
    - "6 dof grasping for target driven object manipulation in clutter"
    - "form2fit learning shape priors for generalizable assembly from disassembly"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "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"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-2 (induced by source paper "guided constrained policy optimization for dynamic quadrupedal robot locomotion"):
  with members:
    - "robust recovery controller for a quadrupedal robot using deep reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "quantifying generalization in reinforcement learning"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "behavioral cloning from observation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "constrained policy optimization"

Connected through hyperedge HE-3 (induced by source paper "a review of modularization techniques in artificial neural networks"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "graph hypernetworks for neural architecture search"
    - "mattnet modular attention network for referring expression comprehension"
    - "relational inductive biases deep learning and graph networks"
    - "a graph to sequence model for amr to text generation"
    - "dense and diverse capsule networks making the capsules learn better"
    - "evolving deep neural networks"
    - "hierarchical semantic segmentation using modular convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "learning to manipulate object collections using grounded state representations"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "deep set prediction networks"
    - "learning agile and dynamic motor skills for legged robots"
    - "relational deep reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-5 (induced by source paper "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"):
  with members:
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "deep drone racing learning agile flight in dynamic environments"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "gibson env real world perception for embodied agents"
    - "experiments in fast autonomous gps denied quadrotor flight"
    - "ultra low power deep learning powered autonomous nano drones"
    - "reinforcement learning for non prehensile manipulation transfer from simulation to physical system"

Connected through hyperedge HE-6 (induced by source paper "attention privileged reinforcement learning for domain transfer"):
  with members:
    - "attention privileged reinforcement learning"
    - "solving rubik s cube with a robot hand"
    - "simultaneously learning vision and feature based control policies for real world ball in a cup"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

Connected through hyperedge HE-7 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks via reward shaping"):
  with members:
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "playing hard exploration games by watching youtube"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "a real time robotic grasp approach with oriented anchor box"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "the costar block stacking dataset learning with workspace constraints"

Connected through hyperedge HE-8 (induced by source paper "the costar block stacking dataset learning with workspace constraints"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "monocular object orientation estimation using riemannian regression and classification networks"
    - "playing hard exploration games by watching youtube"
    - "a real time robotic grasp approach with oriented anchor box"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "shapestacks learning vision based physical intuition for generalised object stacking"

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)