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: "vr goggles for robots real to sim domain adaptation for visual control"
  Abstract: This paper deals with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting the common solutions to the problem by increasing the visual fidelity of synthetic images output from simulators during the training phase, this paper seeks to tackle the problem by translating the real-world image streams back to the synthetic domain during the deployment phase, to make the robot feel at home. We propose this as a lightweight, flexible, and efficient solution for visual control, as 1) no extra transfer steps are required during the expensive training of DRL agents in simulation; 2) the trained DRL agents will not be constrained to being deployable in only one specific real-world environment; 3) the policy training and the transfer operations are decoupled, and can be conducted in parallel. Besides this, we propose a conceptually simple yet very effective shift loss to constrain the consistency between subsequent frames, eliminating the need for optical flow. We validate the shift loss for artistic style transfer for videos and domain adaptation, and validate our visual control approach in real-world robot experiments. A video of our results is available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "divis domain invariant visual servoing for collision free goal reaching"):
  with members:
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "zero shot visual imitation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "speaker follower models for vision and language navigation"
    - "viser visual self regularization"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"

Connected through hyperedge HE-2 (induced by source paper "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"):
  with members:
    - "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"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"

Connected through hyperedge HE-3 (induced by source paper "distillation strategies for proximal policy optimization"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "low precision policy distillation with application to low power real time sensation cognition action loop with neuromorphic computing"
    - "model compression via distillation and quantization"
    - "learning transferable architectures for scalable image recognition"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "convolutional networks for fast energy efficient neuromorphic computing"

Connected through hyperedge HE-4 (induced by source paper "gibson env real world perception for embodied agents"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "gonet a semi supervised deep learning approach for traversability estimation"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "a dataset for developing and benchmarking active vision"

Connected through hyperedge HE-5 (induced by source paper "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "the limits and potentials of deep learning for robotics"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "task embedded control networks for few shot imitation learning"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"

Connected through hyperedge HE-6 (induced by source paper "learning to drive from simulation without real world labels"):
  with members:
    - "video to video synthesis"
    - "multimodal unsupervised image to image translation"
    - "learning end to end autonomous driving using guided auxiliary supervision"
    - "driving policy transfer via modularity and abstraction"
    - "learning to drive in a day"
    - "on offline evaluation of vision based driving models"
    - "semi convolutional operators for instance segmentation"
    - "modular vehicle control for transferring semantic information to unseen weather conditions using gans"

Connected through hyperedge HE-7 (induced by source paper "robustness to out of distribution inputs via task aware generative uncertainty"):
  with members:
    - "gibson env real world perception for embodied agents"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "deep exploration via bootstrapped dqn"
    - "deep variational information bottleneck"

Connected through hyperedge HE-8 (induced by source paper "socially compliant navigation through raw depth inputs with generative adversarial imitation learning"):
  with members:
    - "neural slam learning to explore with external memory"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "generative adversarial imitation learning"
    - "asynchronous methods for deep reinforcement learning"
    - "deep reinforcement learning with successor features for navigation across similar environments"
    - "learning to navigate in complex environments"
    - "deep residual learning for image recognition"

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)