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: "a general framework for structured learning of mechanical systems"
  Abstract: Learning accurate dynamics models is necessary for optimal, compliant control of robotic systems. Current approaches to white-box modeling using analytic parameterizations, or black-box modeling using neural networks, can suffer from high bias or high variance. We address the need for a flexible, gray-box model of mechanical systems that can seamlessly incorporate prior knowledge where it is available, and train expressive function approximators where it is not. We propose to parameterize a mechanical system using neural networks to model its Lagrangian and the generalized forces that act on it. We test our method on a simulated, actuated double pendulum. We show that our method outperforms a naive, black-box model in terms of data-efficiency, as well as performance in model-based reinforcement learning. We also conduct a systematic study of our method's ability to incorporate available prior knowledge about the system to improve data efficiency.

Connected through hyperedge HE-1 (induced by source paper "dissipative symoden encoding hamiltonian dynamics with dissipation and control into deep learning"):
  with members:
    - "symplectic ode net learning hamiltonian dynamics with control"
    - "learning dynamical systems from partial observations"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "hamiltonian neural networks"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "continuous control with deep reinforcement learning"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "encoding physical constraints in differentiable newton euler algorithm"):
  with members:
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "hamiltonian neural networks"
    - "linear matrix inequalities for physically consistent inertial parameter identification a statistical perspective on the mass distribution"
    - "a new data source for inverse dynamics learning"
    - "identification of fully physical consistent inertial parameters using optimization on manifolds"

Connected through hyperedge HE-3 (induced by source paper "symplectic ode net learning hamiltonian dynamics with control"):
  with members:
    - "learning dynamical systems from partial observations"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "hamiltonian neural networks"
    - "graph networks as learnable physics engines for inference and control"
    - "neural ordinary differential equations"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "continuous control with deep reinforcement learning"
    - "deep residual learning for image recognition"

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)