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: "automated derivation of the adjoint of high level transient finite element programs"
  Abstract: In this paper we demonstrate a new technique for deriving discrete adjoint and tangent linear models of a finite element model. The technique is significantly more efficient and automatic than standard algorithmic differentiation techniques. The approach relies on a high-level symbolic representation of the forward problem. In contrast to developing a model directly in Fortran or C++, high-level systems allow the developer to express the variational problems to be solved in near-mathematical notation. As such, these systems have a key advantage: since the mathematical structure of the problem is preserved, they are more amenable to automated analysis and manipulation. The framework introduced here is implemented in a freely available software package named dolfin-adjoint, based on the FEniCS Project. Our approach to automated adjoint derivation relies on run-time annotation of the temporal structure of the model and employs the FEniCS finite element form compiler to automatically generate the low-level co...

Connected through hyperedge HE-1 (induced by source paper "algorithms and software for projections onto intersections of convex and non convex sets with applications to inverse problems"):
  with members:
    - "a unified 2d 3d large scale software environment for nonlinear inverse problems"
    - "adaptive relaxed admm convergence theory and practical implementation"
    - "an inner loop free solution to inverse problems using deep neural networks"
    - "image restoration using autoencoding priors"
    - "adaptive consensus admm for distributed optimization"
    - "devito automated fast finite difference computation"
    - "adaptive admm with spectral penalty parameter selection"
    - "plug and play admm for image restoration fixed point convergence and applications"

Connected through hyperedge HE-2 (induced by source paper "devito v3 1 0 an embedded domain specific language for finite differences and geophysical exploration"):
  with members:
    - "simflowny 2 an upgraded platform for scientific modelling and simulation"
    - "architecture and performance of devito a system for automated stencil computation"
    - "high level python abstractions for optimal checkpointing in inversion problems"
    - "optimised finite difference computation from symbolic equations"
    - "performance prediction of finite difference solvers for different computer architectures"
    - "firedrake automating the finite element method by composing abstractions"
    - "opensbli a framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures"
    - "cross loop optimization of arithmetic intensity for finite element local assembly"

Connected through hyperedge HE-3 (induced by source paper "neural reparameterization improves structural optimization"):
  with members:
    - "global optimization of dielectric metasurfaces using a physics driven neural network"
    - "regularization by architecture a deep prior approach for inverse problems"
    - "3d topology optimization using convolutional neural networks"
    - "neural networks for topology optimization"
    - "deep image prior"
    - "multi content gan for few shot font style transfer"

Connected through hyperedge HE-4 (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"
    - "forward mode automatic differentiation in julia"
    - "automatic differentiation in machine learning a survey"

Connected through hyperedge HE-5 (induced by source paper "gpdoemd a python package for design of experiments for model discrimination"):
  with members:
    - "automatic differentiation in machine learning a survey"
    - "input design for model discrimination and fault detection via convex relaxation"
    - "a widely applicable bayesian information criterion"
    - "asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory"

Connected through hyperedge HE-6 (induced by source paper "abstractions and automated algorithms for mixed domain finite element methods"):
  with members:
    - "finite element simulation of ionicelectrodiffusion in cellular geometries"
    - "porepy an open source simulation tool for flow and transport in deformable fractured rocks"
    - "dolfin automated finite element computing"

Connected through hyperedge HE-7 (induced by source paper "snc a cloud service platform for symbolic numeric computation using just in time compilation"):
  with members:
    - "high performance cloud computing a view of scientific applications"
    - "introduction to the ginac framework for symbolic computation within the c programming language"

Connected through hyperedge HE-8 (induced by source paper "computing derivatives for petsc adjoint solvers using algorithmic differentiation"):
  with members:
    - "high performance derivative computations using codipack"

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)