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 "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"):
  Abstract: The derivatives of differential equation solutions are commonly used as model diagnostics and as part of parameter estimation routines. In this manuscript we investigate an implementation of Discrete local Sensitivity Analysis via Automatic Differentiation (DSAAD). A non-stiff Lotka-Volterra model, a discretization of the two dimensional ($N \times N$) Brusselator stiff reaction-diffusion PDE, a stiff non-linear air pollution and a non-stiff pharmacokinetic/pharmacodynamic (PK/PD) model were used as prototype models for this investigation. Our benchmarks show that on sufficiently small (<100 parameters) stiff and non-stiff systems of ODEs, forward-mode DSAAD is more efficient than both reverse-mode DSAAD and continuous forward/adjoint sensitivity analysis. The scalability of continuous adjoint methods is shown to result in better efficiency for larger ODE systems such as PDE discretizations. In addition to testing efficiency, results on test equations demonstrate the applicability of DSAAD to differential-algebraic equations, delay differential equations, and hybrid differential equation systems where the event timing and effects are dependent on model parameters. Together, these results show that language-level automatic differentiation is an efficient method for calculating local sensitivities of a wide range of differential equation models.

Includes members:
  Member "don t unroll adjoint differentiating ssa form programs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differentiate everything with a reversible programming language")
  Member "neural ordinary differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic latent residual video prediction")
  Member "ffjord free form continuous dynamics for scalable reversible generative models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "forward mode automatic differentiation in julia":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentiate everything with a reversible programming language")

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.NA(Numerical Analysis)