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: "domain randomization and generative models for robotic grasping"
  Abstract: Deep learning-based robotic grasping has made significant progress the past several years thanks to algorithmic improvements and increased data availability. However, state-of-the-art models are often trained on as few as hundreds or thousands of unique object instances, and as a result generalization can be a challenge. #R##N#In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of \emph{domain randomization} to object synthesis. We generate millions of unique, unrealistic procedurally generated objects, and train a deep neural network to perform grasp planning on these objects. #R##N#Since the distribution of successful grasps for a given object can be highly multimodal, we propose an autoregressive grasp planning model that maps sensor inputs of a scene to a probability distribution $p_{\theta}$, where $p_{\theta} (g \mid I)$ corresponds to the model's estimate of the normalized probability of success of the grasp $g$ conditioned on the observations $I$. Our model allows us to sample grasps efficiently at test time (or avoid sampling entirely). #R##N#We evaluate our model architecture and data generation pipeline in simulation and find we can achieve $>$90\% success rate on previously unseen realistic objects at test time despite having only been trained on random objects.

Connected through hyperedge HE-1 (induced by source paper "metagrasp data efficient grasping by affordance interpreter network"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "asymmetric actor critic for image based robot learning"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

Connected through hyperedge HE-2 (induced by source paper "domain randomization for active pose estimation"):
  with members:
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "cad2rl real single image flight without a single real image"
    - "revisiting active perception"
    - "learning contact rich manipulation skills with guided policy search"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "a data efficient framework for training and sim to real transfer of navigation policies"):
  with members:
    - "universal planning networks"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "bayesian model agnostic meta learning"
    - "sim to real learning agile locomotion for quadruped robots"
    - "evolved policy gradients"
    - "probabilistic model agnostic meta learning"
    - "adversarial discriminative sim to real transfer of visuo motor policies"

Connected through hyperedge HE-4 (induced by source paper "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"):
  with members:
    - "learning to augment synthetic images for sim2real policy transfer"
    - "monocular depth estimation a survey"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "minos multimodal indoor simulator for navigation in complex environments"

Connected through hyperedge HE-5 (induced by source paper "lift reinforcement learning in computer systems by learning from demonstrations"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "the case for automatic database administration using deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "a machine learning approach to routing"
    - "rllib abstractions for distributed reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"

Connected through hyperedge HE-6 (induced by source paper "illuminating generalization in deep reinforcement learning through procedural level generation"):
  with members:
    - "deep reinforcement learning for general video game ai"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "automated curriculum learning by rewarding temporally rare events"
    - "a study on overfitting in deep reinforcement learning"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "automated curriculum learning for neural networks"
    - "hindsight experience replay"

Connected through hyperedge HE-7 (induced by source paper "deep semantic instance segmentation of tree like structures using synthetic data"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "pu net point cloud upsampling network"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "semantic instance segmentation with a discriminative loss function"
    - "unsupervised cross domain image generation"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning to acquire navigation skills for wheel legged robots in complex environments"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "hindsight experience replay"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "deep predictive policy training using reinforcement learning"
    - "emergence of locomotion behaviours in rich environments"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "proximal policy optimization algorithms"

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)