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: "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
  Abstract: Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation to produce large amounts of labelled data. However, training models on simulated images does not readily transfer to real-world ones. Using domain adaptation methods to cross this "reality gap" requires a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power. In this paper, we present Randomized-to-Canonical Adaptation Networks (RCANs), a novel approach to crossing the visual reality gap that uses no real-world data. Our method learns to translate randomized rendered images into their equivalent non-randomized, canonical versions. This in turn allows for real images to also be translated into canonical sim images. We demonstrate the effectiveness of this sim-to-real approach by training a vision-based closed-loop grasping reinforcement learning agent in simulation, and then transferring it to the real world to attain 70% zero-shot grasp success on unseen objects, a result that almost doubles the success of learning the same task directly on domain randomization alone. Additionally, by joint finetuning in the real-world with only 5,000 real-world grasps, our method achieves 91%, attaining comparable performance to a state-of-the-art system trained with 580,000 real-world grasps, resulting in a reduction of real-world data by more than 99%.

Connected through hyperedge HE-1 (induced by source paper "adapting object detectors with conditional domain normalization"):
  with members:
    - "compound domain adaptation in an open world"
    - "precise synthetic image and lidar presil dataset for autonomous vehicle perception"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "domain adaptation for structured output via discriminative patch representations"
    - "semantic image synthesis with spatially adaptive normalization"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "pointdan a multi scale 3d domain adaption network for point cloud representation"
    - "moment matching for multi source domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping"):
  with members:
    - "directional semantic grasping of real world objects from simulation to reality"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "metagrasp data efficient grasping by affordance interpreter network"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "acceleration of actor critic deep reinforcement learning for visual grasping in clutter by state representation learning based on disentanglement of a raw input image"):
  with members:
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "domain randomization for active pose estimation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning with linear quadratic regulator regions"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "solving rubik s cube with a robot hand"
    - "assessing transferability from simulation to reality for reinforcement learning"
    - "continuous control with contexts provably"
    - "learning agile and dynamic motor skills for legged robots"
    - "h_ infty model free reinforcement learning with robust stability guarantee"
    - "robust model free reinforcement learning with multi objective bayesian optimization"
    - "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "deep differentiable grasp planner for high dof grippers"):
  with members:
    - "generating grasp poses for a high dof gripper using neural networks"
    - "new formulation of mixed integer conic programming for globally optimal grasp planning"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "difftaichi differentiable programming for physical simulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "chainqueen a real time differentiable physical simulator for soft robotics"

Connected through hyperedge HE-7 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue"):
  with members:
    - "does computer vision matter for action"
    - "learning the depths of moving people by watching frozen people"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning human optical flow"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised learning of object landmarks through conditional image generation"

Connected through hyperedge HE-8 (induced by source paper "robonet large scale multi robot learning"):
  with members:
    - "self supervised exploration via disagreement"
    - "learning latent plans from play"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "manipulation by feel touch based control with deep predictive models"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "unsupervised visuomotor control through distributional planning networks"
    - "scaling autoregressive video models"
    - "exploration by random network distillation"

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)