human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "differential dynamic programming neural optimizer"):
  Abstract: Interpretation of Deep Neural Networks (DNNs) training as an optimal control problem with nonlinear dynamical systems has received considerable attention recently, yet the algorithmic development remains relatively limited. In this work, we make an attempt along this line by reformulating the training procedure from the trajectory optimization perspective. We first show that most widely-used algorithms for training DNNs can be linked to the Differential Dynamic Programming (DDP), a celebrated second-order trajectory optimization algorithm rooted in the Approximate Dynamic Programming. In this vein, we propose a new variant of DDP that can accept batch optimization for training feedforward networks, while integrating naturally with the recent progress in curvature approximation. The resulting algorithm features layer-wise feedback policies which improve convergence rate and reduce sensitivity to hyper-parameter over existing methods. We show that the algorithm is competitive against state-ofthe-art first and second order methods. Our work opens up new avenues for principled algorithmic design built upon the optimal control theory.

Includes members:
  Member "symplectic ode net learning hamiltonian dynamics with control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dissipative symoden encoding hamiltonian dynamics with dissipation and control into deep learning")
  Member "painless stochastic gradient interpolation line search and convergence rates":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization")
  Member "feedback network for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "feedback graph convolutional network for skeleton based action recognition")
  Member "understanding and improving transformer from a multi particle dynamic system point of view":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on layer normalization in the transformer architecture")
  Member "neural sde stabilizing neural ode networks with stochastic noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable gradients for stochastic differential equations")
  Member "deep learning theory review an optimal control and dynamical systems perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pontryagin differentiable programming an end to end learning and control framework")
  Member "negative eigenvalues of the hessian in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a closer look at the optimization landscapes of generative adversarial networks")
  Member "hamiltonian neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "automatic differentiation and continuous sensitivity analysis of rigid body dynamics")

Question: Please tell me which class the source paper 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)