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 "tunenet one shot residual tuning for system identification and sim to real robot task transfer"):
  Abstract: As researchers teach robots to perform more and more complex tasks, the need for realistic simulation environments is growing. Existing techniques for closing the reality gap by approximating real-world physics often require extensive real world data and/or thousands of simulation samples. This paper presents TuneNet, a new machine learning-based method to directly tune the parameters of one model to match another using an $\textit{iterative residual tuning}$ technique. TuneNet estimates the parameter difference between two models using a single observation from the target and minimal simulation, allowing rapid, accurate and sample-efficient parameter estimation. The system can be trained via supervised learning over an auto-generated simulated dataset. We show that TuneNet can perform system identification, even when the true parameter values lie well outside the distribution seen during training, and demonstrate that simulators tuned with TuneNet outperform existing techniques for predicting rigid body motion. Finally, we show that our method can estimate real-world parameter values, allowing a robot to perform sim-to-real task transfer on a dynamic manipulation task unseen during training. We are also making a baseline implementation of our code available online.

Includes members:
  Member "bayessim adaptive domain randomization via probabilistic inference for robotics simulators":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disco double likelihood free inference stochastic control")
  Member "densephysnet learning dense physical object representations via multi step dynamic interactions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "tossingbot learning to throw arbitrary objects with residual physics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "residual policy learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "brpo batch residual policy optimization")
  Member "friction variability in auto collected dataset of planar pushing experiments and anisotropic friction":
    also connected through other hyperedges:
      (none)
  Member "sim to real learning agile locomotion for quadruped robots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "closing the sim to real loop adapting simulation randomization with real world experience":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "predicting the physical dynamics of unseen 3d objects")

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)