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: "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
  Abstract: We introduce BayesSim, a framework for robotics simulations allowing a full Bayesian treatment for the parameters of the simulator. As simulators become more sophisticated and able to represent the dynamics more accurately, fundamental problems in robotics such as motion planning and perception can be solved in simulation and solutions transferred to the physical robot. However, even the most complex simulator might still not be able to represent reality in all its details either due to inaccurate parametrization or simplistic assumptions in the dynamic models. BayesSim provides a principled framework to reason about the uncertainty of simulation parameters. Given a black box simulator (or generative model) that outputs trajectories of state and action pairs from unknown simulation parameters, followed by trajectories obtained with a physical robot, we develop a likelihood-free inference method that computes the posterior distribution of simulation parameters. This posterior can then be used in problems where Sim2Real is critical, for example in policy search. We compare the performance of BayesSim in obtaining accurate posteriors in a number of classical control and robotics problems. Results show that the posterior computed from BayesSim can be used for domain randomization outperforming alternative methods that randomize based on uniform priors.

Connected through hyperedge HE-1 (induced by source paper "disco double likelihood free inference stochastic control"):
  with members:
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "uncertainty averse pushing with model predictive path integral control"
    - "online adaptation of deep architectures with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "inferring the material properties of granular media for robotic tasks"):
  with members:
    - "non smooth newton methods for deformable multi body dynamics"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "gpu accelerated robotic simulation for distributed reinforcement learning"
    - "learning robotic manipulation of granular media"

Connected through hyperedge HE-3 (induced by source paper "vid2param modelling of dynamics parameters from video"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "physics as inverse graphics joint unsupervised learning of objects and physics from video"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "active localization of gas leaks using fluid simulation"
    - "interpretable latent spaces for learning from demonstration"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "the orca hub explainable offshore robotics through intelligent interfaces"

Connected through hyperedge HE-4 (induced by source paper "vid2param online system identification from video for robotics applications"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "physics as inverse graphics joint unsupervised learning of objects and physics from video"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "interpretable latent spaces for learning from demonstration"
    - "neural lander stable drone landing control using learned dynamics"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"

Connected through hyperedge HE-5 (induced by source paper "tunenet one shot residual tuning for system identification and sim to real robot task transfer"):
  with members:
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "residual policy learning"
    - "friction variability in auto collected dataset of planar pushing experiments and anisotropic friction"
    - "sim to real learning agile locomotion for quadruped robots"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "residual reinforcement learning for robot control"

Connected through hyperedge HE-6 (induced by source paper "robel robotics benchmarks for learning with low cost robots"):
  with members:
    - "replab a reproducible low cost arm benchmark platform for robotic learning"
    - "active domain randomization"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "sim to real reinforcement learning for deformable object manipulation"

Connected through hyperedge HE-7 (induced by source paper "gym ignition reproducible robotic simulations for reinforcement learning"):
  with members:
    - "modelling generalized forces with reinforcement learning for sim to real transfer"
    - "hierarchical reinforcement learning for quadruped locomotion"
    - "assessing transferability from simulation to reality for reinforcement learning"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "gym gazebo2 a toolkit for reinforcement learning using ros 2 and gazebo"
    - "learning agile and dynamic motor skills for legged robots"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-8 (induced by source paper "multi agent manipulation via locomotion using hierarchical sim2real"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "active domain randomization"
    - "hierarchical reinforcement learning for quadruped locomotion"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

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)