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: "maximum principle based algorithms for deep learning"
  Abstract: The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms. Training is recast as a control problem and this allows us to formulate necessary optimality conditions in continuous time using the Pontryagin's maximum principle (PMP). A modification of the method of successive approximations is then used to solve the PMP, giving rise to an alternative training algorithm for deep learning. This approach has the advantage that rigorous error estimates and convergence results can be established. We also show that it may avoid some pitfalls of gradient-based methods, such as slow convergence on flat landscapes near saddle points. Furthermore, we demonstrate that it obtains favorable initial convergence rate per-iteration, provided Hamiltonian maximization can be efficiently carried out - a step which is still in need of improvement. Overall, the approach opens up new avenues to attack problems associated with deep learning, such as trapping in slow manifolds and inapplicability of gradient-based methods for discrete trainable variables.

Connected through hyperedge HE-1 (induced by source paper "differential dynamic programming neural optimizer"):
  with members:
    - "symplectic ode net learning hamiltonian dynamics with control"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "feedback network for image super resolution"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "deep learning theory review an optimal control and dynamical systems perspective"
    - "negative eigenvalues of the hessian in deep neural networks"
    - "hamiltonian neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep learning theory review an optimal control and dynamical systems perspective"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "a mean field theory of batch normalization"
    - "where is the information in a deep neural network"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"

Connected through hyperedge HE-3 (induced by source paper "dynamical system inspired adaptive time stepping controller for residual network families"):
  with members:
    - "towards robust resnet a small step but a giant leap"
    - "imexnet a forward stable deep neural network"
    - "smooth inter layer propagation of stabilized neural networks for classification"
    - "convolutional neural networks with alternately updated clique"
    - "spectral normalization for generative adversarial networks"
    - "invertible residual networks"
    - "optimization algorithm inspired deep neural network structure design"
    - "stable architectures for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "review ordinary differential equations for deep learning"):
  with members:
    - "machine learning error models for approximate solutions to parameterized systems of nonlinear equations"
    - "monge amp ere flow for generative modeling"
    - "deep sets"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "an optimal control approach to deep learning and applications to discrete weight neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep learning via dynamical systems an approximation perspective"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "nonlinear approximation via compositions"
    - "barron spaces and the compositional function spaces for neural network models"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "a priori estimates of the population risk for residual networks"
    - "approximation capabilities of neural ordinary differential equations"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"

Connected through hyperedge HE-6 (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"
    - "path integral networks end to end differentiable optimal control"
    - "deep learning approximation for stochastic control problems"
    - "value iteration networks"
    - "control of memory active perception and action in minecraft"

Connected through hyperedge HE-7 (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"
    - "deep learning approximation for stochastic control problems"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "neural stochastic differential equations deep latent gaussian models in the diffusion limit"):
  with members:
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "neural jump stochastic differential equations"
    - "diffeqflux jl a julia library for neural differential equations"
    - "deep learning with differential gaussian process flows"
    - "stable architectures for deep neural networks"
    - "neural ordinary differential equations"
    - "identity matters in deep 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.LG(Machine Learning)