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 "neural stochastic differential equations deep latent gaussian models in the diffusion limit"):
  Abstract: In deep latent Gaussian models, the latent variable is generated by a time-inhomogeneous Markov chain, where at each time step we pass the current state through a parametric nonlinear map, such as a feedforward neural net, and add a small independent Gaussian perturbation. This work considers the diffusion limit of such models, where the number of layers tends to infinity, while the step size and the noise variance tend to zero. The limiting latent object is an Ito diffusion process that solves a stochastic differential equation (SDE) whose drift and diffusion coefficient are implemented by neural nets. We develop a variational inference framework for these \textit{neural SDEs} via stochastic automatic differentiation in Wiener space, where the variational approximations to the posterior are obtained by Girsanov (mean-shift) transformation of the standard Wiener process and the computation of gradients is based on the theory of stochastic flows. This permits the use of black-box SDE solvers and automatic differentiation for end-to-end inference. Experimental results with synthetic data are provided.

Includes members:
  Member "neural sde stabilizing neural ode networks with stochastic noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differential dynamic programming neural optimizer")
  Member "neural jump stochastic differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")
  Member "diffeqflux jl a julia library for neural differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximation capabilities of neural odes and invertible residual networks")
  Member "deep learning with differential gaussian process flows":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable gradients for stochastic differential equations")
  Member "stable architectures for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")
  Member "neural ordinary differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stochastic latent residual video prediction")
  Member "maximum principle based algorithms for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "differential dynamic programming neural optimizer")
  Member "identity matters in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "backward feature correction how deep learning performs deep learning")

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)