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 "automatic differentiation and continuous sensitivity analysis of rigid body dynamics"):
  Abstract: A key ingredient to achieving intelligent behavior is physical understanding that equips robots with the ability to reason about the effects of their actions in a dynamic environment. Several methods have been proposed to learn dynamics models from data that inform model-based control algorithms. While such learning-based approaches can model locally observed behaviors, they fail to generalize to more complex dynamics and under long time horizons. #R##N#In this work, we introduce a differentiable physics simulator for rigid body dynamics. Leveraging various techniques for differential equation integration and gradient calculation, we compare different methods for parameter estimation that allow us to infer the simulation parameters that are relevant to estimation and control of physical systems. In the context of trajectory optimization, we introduce a closed-loop model-predictive control algorithm that infers the simulation parameters through experience while achieving cost-minimizing performance.

Includes members:
  Member "densephysnet learning dense physical object representations via multi step dynamic interactions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "deep lagrangian networks using physics as model prior for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "hamiltonian neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differential dynamic programming neural optimizer")
  Member "deepmind control suite":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural jump stochastic differential equations")
  Member "learning particle dynamics for manipulating rigid bodies deformable objects and fluids":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "closing the sim to real loop adapting simulation randomization with real world experience":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "flexible neural representation for physics prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relational state space model for stochastic multi object systems")

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)