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: "deep lagrangian networks using physics as model prior for deep learning"
  Abstract: Deep learning has achieved astonishing results on many tasks with large amounts of data and generalization within the proximity of training data. For many important real-world applications, these requirements are unfeasible and additional prior knowledge on the task domain is required to overcome the resulting problems. In particular, learning physics models for model-based control requires robust extrapolation from fewer samples - often collected online in real-time - and model errors may lead to drastic damages of the system. Directly incorporating physical insight has enabled us to obtain a novel deep model learning approach that extrapolates well while requiring fewer samples. As a first example, we propose Deep Lagrangian Networks (DeLaN) as a deep network structure upon which Lagrangian Mechanics have been imposed. DeLaN can learn the equations of motion of a mechanical system (i.e., system dynamics) with a deep network efficiently while ensuring physical plausibility. The resulting DeLaN network performs very well at robot tracking control. The proposed method did not only outperform previous model learning approaches at learning speed but exhibits substantially improved and more robust extrapolation to novel trajectories and learns online in real-time

Connected through hyperedge HE-1 (induced by source paper "learning to fly via deep model based reinforcement learning"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "solving rubik s cube with a robot hand"
    - "imagined value gradients model based policy optimization with transferable latent dynamics models"
    - "flying through a narrow gap using neural network an end to end planning and control approach"
    - "teach repeat replan a complete and robust system for aggressive flight in complex environments"
    - "neuroflight next generation flight control firmware"
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"
    - "real time reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "automatic differentiation and continuous sensitivity analysis of rigid body dynamics"):
  with members:
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "hamiltonian neural networks"
    - "deepmind control suite"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "flexible neural representation for physics prediction"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-3 (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"
    - "a general framework for structured learning of mechanical systems"
    - "learning dynamical systems from partial observations"
    - "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-4 (induced by source paper "lagrangian neural networks"):
  with members:
    - "hamiltonian graph networks with ode integrators"
    - "symplectic recurrent neural networks"
    - "hamiltonian neural networks"
    - "phyre a new benchmark for physical reasoning"
    - "interaction networks for learning about objects relations and physics"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "encoding physical constraints in differentiable newton euler algorithm"):
  with members:
    - "a general framework for structured learning of mechanical systems"
    - "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-6 (induced by source paper "meta learning via learned loss"):
  with members:
    - "guided meta policy search"
    - "reward shaping via meta learning"
    - "generalized inner loop meta learning"
    - "meta gradient reinforcement learning"
    - "unsupervised learning via meta learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "evolved policy gradients"
    - "one shot imitation from observing humans via domain adaptive meta learning"

Connected through hyperedge HE-7 (induced by source paper "hamiltonian neural networks"):
  with members:
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "reversible recurrent neural networks"
    - "neural ordinary differential equations"
    - "i revnet deep invertible networks"
    - "relational inductive bias for physical construction in humans and machines"
    - "recurrent world models facilitate policy evolution"

Connected through hyperedge HE-8 (induced by source paper "wasserstein robust reinforcement learning"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "compatible natural gradient policy search"
    - "non stationary markov decision processes a worst case approach using model based reinforcement learning"
    - "action robust reinforcement learning and applications in continuous control"
    - "beyond confidence regions tight bayesian ambiguity sets for robust mdps"
    - "deep lagrangian networks for end to end learning of energy based control for under actuated systems"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "lipschitz continuity in model based reinforcement learning"

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.LG(Machine Learning)