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: "a differentiable programming system to bridge machine learning and scientific computing"
  Abstract: Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large amounts of data. At the same time, machine learning models are becoming increasingly sophisticated and exhibit many features often seen in scientific computing, stressing the capabilities of machine learning frameworks. Just as the disciplines of scientific computing and machine learning have shared common underlying infrastructure in the form of numerical linear algebra, we now have the opportunity to further share new computational infrastructure, and thus ideas, in the form of Differentiable Programming. We describe Zygote, a Differentiable Programming system that is able to take gradients of general program structures. We implement this system in the Julia programming language. Our system supports almost all language constructs (control flow, recursion, mutation, etc.) and compiles high-performance code without requiring any user intervention or refactoring to stage computations. This enables an expressive programming model for deep learning, but more importantly, it enables us to incorporate a large ecosystem of libraries in our models in a straightforward way. We discuss our approach to automatic differentiation, including its support for advanced techniques such as mixed-mode, complex and checkpointed differentiation, and present several examples of differentiating programs.

Connected through hyperedge HE-1 (induced by source paper "differentiate everything with a reversible programming language"):
  with members:
    - "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"
    - "forward mode automatic differentiation in julia"

Connected through hyperedge HE-2 (induced by source paper "scalable gradients for stochastic differential equations"):
  with members:
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "latent odes for irregularly sampled time series"
    - "neural jump stochastic differential equations"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "deep learning with differential gaussian process flows"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-3 (induced by source paper "dynamicppl stan like speed for dynamic probabilistic models"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "julia a fresh approach to numerical computing"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"

Connected through hyperedge HE-4 (induced by source paper "universal differential equations for scientific machine learning"):
  with members:
    - "fashionable modelling with flux"
    - "forward mode automatic differentiation in julia"

Connected through hyperedge HE-5 (induced by source paper "graph neural ordinary differential equations"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "latent odes for irregularly sampled time series"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hamiltonian graph networks with ode integrators"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "fast and deep graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "neural jump stochastic differential equations"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "diffeqflux jl a julia library for neural differential equations"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "solving for high dimensional committor functions using artificial neural networks"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "a dirichlet mixture model of hawkes processes for event sequence clustering"

Connected through hyperedge HE-7 (induced by source paper "difftaichi differentiable programming for physical simulation"):
  with members:
    - "chainqueen a real time differentiable physical simulator for soft robotics"
    - "flexible neural representation for physics prediction"
    - "spnets differentiable fluid dynamics for deep neural networks"
    - "neural 3d mesh renderer"
    - "a differentiable physics engine for deep learning in robotics"
    - "interaction networks for learning about objects relations and physics"
    - "a compositional object based approach to learning physical dynamics"

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.PL(Programming Languages)