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: "the stan math library reverse mode automatic differentiation in c"
  Abstract: As computational challenges in optimization and statistical inference grow ever harder, algorithms that utilize derivatives are becoming increasingly more important. The implementation of the derivatives that make these algorithms so powerful, however, is a substantial user burden and the practicality of these algorithms depends critically on tools like automatic differentiation that remove the implementation burden entirely. The Stan Math Library is a C++, reverse-mode automatic differentiation library designed to be usable, extensive and extensible, efficient, scalable, stable, portable, and redistributable in order to facilitate the construction and utilization of such algorithms. #R##N#Usability is achieved through a simple direct interface and a cleanly abstracted functional interface. The extensive built-in library includes functions for matrix operations, linear algebra, differential equation solving, and most common probability functions. Extensibility derives from a straightforward object-oriented framework for expressions, allowing users to easily create custom functions. Efficiency is achieved through a combination of custom memory management, subexpression caching, traits-based metaprogramming, and expression templates. Partial derivatives for compound functions are evaluated lazily for improved scalability. Stability is achieved by taking care with arithmetic precision in algebraic expressions and providing stable, compound functions where possible. For portability, the library is standards-compliant C++ (03) and has been tested for all major compilers for Windows, Mac OS X, and Linux.

Connected through hyperedge HE-1 (induced by source paper "automatic differentiation and continuous sensitivity analysis of rigid body dynamics"):
  with members:
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "hamiltonian neural networks"
    - "deepmind control suite"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-2 (induced by source paper "correctness of automatic differentiation via diffeologies and categorical gluing"):
  with members:
    - "backpropagation in the simply typed lambda calculus with linear negation"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "the simple essence of automatic differentiation"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "interactive differentiable simulation"):
  with members:
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "deepmind control suite"
    - "graph networks as learnable physics engines for inference and control"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "flexible neural representation for physics prediction"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "spnets differentiable fluid dynamics for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "static analysis for probabilistic programs"):
  with members:
    - "probabilistic programming with densities in slicstan efficient flexible and deterministic"
    - "pyro deep universal probabilistic programming"
    - "autoconj recognizing and exploiting conjugacy without a domain specific language"
    - "a lambda calculus foundation for universal probabilistic programming"
    - "church a language for generative models"

Connected through hyperedge HE-5 (induced by source paper "physics based simulation of continuous wave lidar for localization calibration and tracking"):
  with members:
    - "interactive differentiable simulation"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "automatic differentiation of rigid body dynamics for optimal control and estimation"

Connected through hyperedge HE-6 (induced by source paper "a review of automatic differentiation and its efficient implementation"):
  with members:
    - "high performance derivative computations using codipack"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "efficient expression templates for operator overloading based automatic differentiation"

Connected through hyperedge HE-7 (induced by source paper "eigen ad algorithmic differentiation of the eigen library"):
  with members:
    - "high performance derivative computations using codipack"
    - "expression templates revisited a performance analysis of current methodologies"

Connected through hyperedge HE-8 (induced by source paper "model inference for ordinary differential equations by parametric polynomial kernel regression"):
  with members:
    - "adam a method for stochastic optimization"

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)