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: "forward mode automatic differentiation in julia"
  Abstract: We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring performance competitive with low-level languages like C++. Unlike recently developed AD tools in other popular high-level languages such as Python and MATLAB, ForwardDiff takes advantage of just-in-time (JIT) compilation to transparently recompile AD-unaware user code, enabling efficient support for higher-order differentiation and differentiation using custom number types (including complex numbers). For gradient and Jacobian calculations, ForwardDiff provides a variant of vector-forward mode that avoids expensive heap allocation and makes better use of memory bandwidth than traditional vector mode. In our numerical experiments, we demonstrate that for nontrivially large dimensions, ForwardDiff's gradient computations can be faster than a reverse-mode implementation from the Python-based autograd package. We also illustrate how ForwardDiff is used effectively within JuMP, a modeling language for optimization. According to our usage statistics, 41 unique repositories on GitHub depend on ForwardDiff, with users from diverse fields such as astronomy, optimization, finite element analysis, and statistics.

Connected through hyperedge HE-1 (induced by source paper "differentiate everything with a reversible programming language"):
  with members:
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "effective extensible programming unleashing julia on gpus"
    - "don t unroll adjoint differentiating ssa form programs"
    - "invertible residual networks"
    - "fashionable modelling with flux"
    - "auto differentiating linear algebra"
    - "design and implementation of a reversible object oriented programming language"
    - "density estimation using real nvp"

Connected through hyperedge HE-2 (induced by source paper "flexible numerical optimization with ensmallen"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "practical sparse matrices in c with hybrid storage and template based expression optimisation"
    - "a user friendly hybrid sparse matrix class in c"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "ensmallen a flexible c library for efficient function optimization"
    - "improving generalization performance by switching from adam to sgd"
    - "training gans with optimism"

Connected through hyperedge HE-3 (induced by source paper "universal differential equations for scientific machine learning"):
  with members:
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "fashionable modelling with flux"

Connected through hyperedge HE-4 (induced by source paper "reproducibility challenge neurips 2019 report on competitive gradient descent"):
  with members:
    - "the numerics of gans"
    - "training gans with optimism"

Connected through hyperedge HE-5 (induced by source paper "a differentiable augmented lagrangian method for bilevel nonlinear optimization"):
  with members:
    - "reduced order model predictive control for setpoint tracking"
    - "optnet differentiable optimization as a layer in neural networks"
    - "a differentiable physics engine for deep learning in robotics"
    - "input convex neural networks"
    - "julia a fast dynamic language for technical computing"

Connected through hyperedge HE-6 (induced by source paper "automatic differentiation for adjoint stencil loops"):
  with members:
    - "don t unroll adjoint differentiating ssa form programs"
    - "architecture and performance of devito a system for automated stencil computation"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "automatic differentiation in machine learning a survey"
    - "automated derivation of the adjoint of high level transient finite element programs"

Connected through hyperedge HE-7 (induced by source paper "diffeqflux jl a julia library for neural differential equations"):
  with members:
    - "don t unroll adjoint differentiating ssa form programs"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "neural ordinary differential equations"

Connected through hyperedge HE-8 (induced by source paper "dynamic automatic differentiation of gpu broadcast kernels"):
  with members:
    - "confusion of tagged perturbations in forward automatic differentiation of higher order functions"
    - "effective extensible programming unleashing julia on gpus"
    - "don t unroll adjoint differentiating ssa form programs"
    - "automatic full compilation of julia programs and ml models to cloud tpus"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "the simple essence of automatic differentiation"
    - "tangent automatic differentiation using source code transformation in python"
    - "hierarchical multiscale recurrent neural networks"

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.MS(Mathematical Software)