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: "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.

Connected through hyperedge HE-1 (induced by source paper "dissecting neural odes"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "imexnet a forward stable deep neural network"
    - "anodev2 a coupled neural ode evolution framework"
    - "deep learning via dynamical systems an approximation perspective"
    - "state space representations of deep neural networks"
    - "neural jump stochastic differential equations"
    - "deep equilibrium models"
    - "port hamiltonian approach to neural network training"

Connected through hyperedge HE-2 (induced by source paper "scalable gradients for stochastic differential equations"):
  with members:
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "latent odes for irregularly sampled time series"
    - "neural jump stochastic differential equations"
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "deep learning with differential gaussian process flows"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-3 (induced by source paper "neural jump stochastic differential equations"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "diffeqflux jl a julia library for neural differential equations"
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "solving for high dimensional committor functions using artificial neural networks"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "a dirichlet mixture model of hawkes processes for event sequence clustering"

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)