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 "stable neural flows"):
  Abstract: We introduce a provably stable variant of neural ordinary differential equations (neural ODEs) whose trajectories evolve on an energy functional parametrised by a neural network. Stable neural flows provide an implicit guarantee on asymptotic stability of the depth-flows, leading to robustness against input perturbations and low computational burden for the numerical solver. The learning procedure is cast as an optimal control problem, and an approximate solution is proposed based on adjoint sensivity analysis. We further introduce novel regularizers designed to ease the optimization process and speed up convergence. The proposed model class is evaluated on non-linear classification and function approximation tasks.

Includes members:
  Member "universal differential equations for scientific machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dissecting neural odes")
  Member "dissecting neural odes":
    also connected through other hyperedges:
      (none)
  Member "scalable gradients for stochastic differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modeling continuous stochastic processes with dynamic normalizing flows")
  Member "a survey of optimization methods from a machine learning perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compositional adam an adaptive compositional solver")
  Member "neural jump stochastic differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")
  Member "graph neural ordinary differential equations":
    also connected through other hyperedges:
      (none)
  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 "beyond finite layer neural networks bridging deep architectures and numerical differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stochastic latent residual video prediction")

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)