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: "genesis rt generating synthetic images for training secondary real world tasks"
  Abstract: We propose a novel approach for generating high-quality, synthetic data for domain-specific learning tasks, for which training data may not be readily available. We leverage recent progress in image-to-image translation to bridge the gap between simulated and real images, allowing us to generate realistic training data for real-world tasks using only unlabeled real-world images and a simulation. GeneSIS-RT ameliorates the burden of having to collect labeled real-world images and is a promising candidate for generating high-quality, domain-specific, synthetic data. #R##N#To show the effectiveness of using GeneSIS-RT to create training data, we study two tasks: semantic segmentation and reactive obstacle avoidance. We demonstrate that learning algorithms trained using data generated by GeneSIS-RT make high-accuracy predictions and outperform systems trained on raw simulated data alone, and as well or better than those trained on real data. Finally, we use our data to train a quadcopter to fly 60 meters at speeds up to 3.4 m/s through a cluttered environment, demonstrating that our GeneSIS-RT images can be used to learn to perform mission-critical tasks.

Connected through hyperedge HE-1 (induced by source paper "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "f1 10 an open source autonomous cyber physical platform"
    - "mushr a low cost open source robotic racecar for education and research"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "self driving scale car trained by deep reinforcement learning"
    - "an online learning approach to model predictive control"
    - "learning agile and dynamic motor skills for legged robots"
    - "demonstration guided deep reinforcement learning of control policies for dexterous human robot interaction"

Connected through hyperedge HE-2 (induced by source paper "divis domain invariant visual servoing for collision free goal reaching"):
  with members:
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "restyling data application to unsupervised domain adaptation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "a kernel theory of modern data augmentation"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "a domain agnostic normalization layer for unsupervised adversarial domain adaptation"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "artistic object recognition by unsupervised style adaptation"
    - "style augmentation data augmentation via style randomization"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "self supervised sim to real adaptation for visual robotic manipulation"):
  with members:
    - "simultaneously learning vision and feature based control policies for real world ball in a cup"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "playing hard exploration games by watching youtube"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "closing the sim to real loop adapting simulation randomization with real world experience"

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:
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "physics based scene level reasoning for object pose estimation in clutter"):
  with members:
    - "fusion volumetric object level slam"
    - "bop benchmark for 6d object pose estimation"
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "6d pose estimation using an improved method based on point pair features"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "rotational subgroup voting and pose clustering for robust 3d object recognition"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "vr goggles for robots real to sim domain adaptation for visual control"):
  with members:
    - "artistic style transfer for videos and spherical images"
    - "transfer learning from synthetic to real images using variational autoencoders for precise position detection"
    - "sem gan semantically consistent image to image translation"
    - "curiosity driven exploration for mapless navigation with deep reinforcement learning"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "cycada cycle consistent adversarial domain adaptation"
    - "carla an open urban driving simulator"

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)