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 dynamical modeling and control of unsteady fluid flows"
  Abstract: The design of flow control systems remains a challenge due to the nonlinear nature of the equations that govern fluid flow. However, recent advances in computational fluid dynamics (CFD) have enabled the simulation of complex fluid flows with high accuracy, opening the possibility of using learning-based approaches to facilitate controller design. We present a method for learning the forced and unforced dynamics of airflow over a cylinder directly from CFD data. The proposed approach, grounded in Koopman theory, is shown to produce stable dynamical models that can predict the time evolution of the cylinder system over extended time horizons. Finally, by performing model predictive control with the learned dynamical models, we are able to find a straightforward, interpretable control law for suppressing vortex shedding in the wake of the cylinder.

Connected through hyperedge HE-1 (induced by source paper "neural network architectures for stochastic control using the nonlinear feynman kac lemma"):
  with members:
    - "mpc inspired neural network policies for sequential decision making"
    - "differentiable mpc for end to end planning and control"
    - "deep learning approximation for stochastic control problems"
    - "learning optimal control via forward and backward stochastic differential equations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "latent space physics towards learning the temporal evolution of fluid flow"):
  with members:
    - "tempogan a temporally coherent volumetric gan for super resolution fluid flow"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "liquid splash modeling with neural networks"
    - "deep learning the physics of transport phenomena"
    - "deep photo style transfer"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"
    - "generating liquid simulations with deformation aware neural networks"

Connected through hyperedge HE-3 (induced by source paper "model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders"):
  with members:
    - "conservative model reduction for finite volume models"
    - "learning koopman invariant subspaces for dynamic mode decomposition"
    - "galerkin v least squares petrov galerkin projection in nonlinear model reduction"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "adaptive h refinement for reduced order models"

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.CE(Computational Engineering, Finance, and Science)