human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "using simulation and domain adaptation to improve efficiency of deep robotic grasping":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "adversarial discriminative sim to real transfer of visuo motor policies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")
  Member "intel realsense stereoscopic depth cameras":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "infrared and 3d skeleton feature fusion for rgb d action recognition")
  Member "learning 6 dof grasping interaction with deep geometry aware 3d representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "6 dof graspnet variational grasp generation for object manipulation")
  Member "domain randomization for transferring deep neural networks from simulation to the real world":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning pregrasp manipulation of objects from ungraspable poses")
  Member "learning a visuomotor controller for real world robotic grasping using easily simulated depth images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "domain independent unsupervised learning to grasp the novel objects")
  Member "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")

Question: Please tell me which class the source paper 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)