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 learning approximation for stochastic control problems"
  Abstract: Many real world stochastic control problems suffer from the "curse of dimensionality". To overcome this difficulty, we develop a deep learning approach that directly solves high-dimensional stochastic control problems based on Monte-Carlo sampling. We approximate the time-dependent controls as feedforward neural networks and stack these networks together through model dynamics. The objective function for the control problem plays the role of the loss function for the deep neural network. We test this approach using examples from the areas of optimal trading and energy storage. Our results suggest that the algorithm presented here achieves satisfactory accuracy and at the same time, can handle rather high dimensional problems.

Connected through hyperedge HE-1 (induced by source paper "pontryagin differentiable programming an end to end learning and control framework"):
  with members:
    - "deep learning theory review an optimal control and dynamical systems perspective"
    - "mpc inspired neural network policies for sequential decision making"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "an optimal control approach to deep learning and applications to discrete weight neural networks"
    - "maximum principle based algorithms for deep learning"
    - "path integral networks end to end differentiable optimal control"
    - "value iteration networks"
    - "control of memory active perception and action in minecraft"

Connected through hyperedge HE-2 (induced by source paper "a machine learning framework for solving high dimensional mean field game and mean field control problems"):
  with members:
    - "fluid flow mass transport for generative networks"
    - "potential flow generator with l_2 optimal transport regularity for generative models"
    - "stable architectures for deep neural networks"
    - "an integral control formulation of mean field game based large scale coordination of loads in smart grids"
    - "a lagrangian gauss newton krylov solver for mass and intensity preserving diffeomorphic image registration"
    - "visualizing the loss landscape of neural nets"
    - "maximum principle based algorithms for deep learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "multi scale deep neural networks for solving high dimensional pdes"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "explicitizing an implicit bias of the frequency principle in two layer neural networks"
    - "theory of the frequency principle for general deep neural networks"
    - "a phase shift deep neural network for high frequency approximation and wave problems"
    - "training behavior of deep neural network in frequency domain"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "neural network architectures for stochastic control using the nonlinear feynman kac lemma"):
  with members:
    - "deep dynamical modeling and control of unsteady fluid flows"
    - "mpc inspired neural network policies for sequential decision making"
    - "differentiable mpc for end to end planning and control"
    - "learning optimal control via forward and backward stochastic differential equations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "deep 2fbsdes for systems with control multiplicative noise"):
  with members:
    - "neural network architectures for stochastic control using the nonlinear feynman kac lemma"
    - "learning optimal control via forward and backward stochastic differential equations"
    - "adam a method for stochastic optimization"
    - "an incremental sampling based algorithm for stochastic optimal control"

Connected through hyperedge HE-6 (induced by source paper "a deep neural network surrogate for high dimensional random partial differential equations"):
  with members:
    - "imposing hard constraints on deep networks promises and limitations"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "an overview of gradient descent optimization algorithms"
    - "deep residual learning for image recognition"
    - "trust region policy optimization"
    - "automatic differentiation in machine learning a survey"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "monge amp ere flow for generative modeling"):
  with members:
    - "residual networks as geodesic flows of diffeomorphisms"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "a flow model of neural networks"
    - "maximum principle based algorithms for deep learning"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "made masked autoencoder for distribution estimation"

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)