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: "learning dynamical systems from partial observations"
  Abstract: We consider the problem of forecasting complex, nonlinear space-time processes when observations provide only partial information of on the system's state. We propose a natural data-driven framework, where the system's dynamics are modelled by an unknown time-varying differential equation, and the evolution term is estimated from the data, using a neural network. Any future state can then be computed by placing the associated differential equation in an ODE solver. We first evaluate our approach on shallow water and Euler simulations. We find that our method not only demonstrates high quality long-term forecasts, but also learns to produce hidden states closely resembling the true states of the system, without direct supervision on the latter. Additional experiments conducted on challenging, state of the art ocean simulations further validate our findings, while exhibiting notable improvements over classical baselines.

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"
    - "a general framework for structured learning of mechanical systems"
    - "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 "inferring dynamical systems with long range dependencies through line attractor regularization"):
  with members:
    - "gated recurrent units viewed through the lens of continuous time dynamical systems"
    - "identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fmri"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "state space lstm models with particle mcmc inference"
    - "gated orthogonal recurrent units on learning to forget"
    - "recurrent orthogonal networks and long memory tasks"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "symplectic ode net learning hamiltonian dynamics with control"):
  with members:
    - "a general framework for structured learning of mechanical systems"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "system identification with time aware neural sequence models"):
  with members:
    - "character level recurrent neural networks in practice comparing training and sampling schemes"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "convolutional neural networks combined with runge kutta methods"
    - "neural ordinary differential equations"
    - "recent trends in deep learning based natural language processing"
    - "recurrent neural networks for multivariate time series with missing values"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "learning generalized quasi geostrophic models using deep neural numerical models"):
  with members:
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "tensorflow a system for large scale machine learning"
    - "automatic differentiation in machine learning a survey"

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.SY(Systems and Control)