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: "don t unroll adjoint differentiating ssa form programs"
  Abstract: This paper presents reverse-mode algorithmic differentiation (AD) based on source code transformation, in particular of the Static Single Assignment (SSA) form used by modern compilers. The approach can support control flow, nesting, mutation, recursion, data structures, higher-order functions, and other language constructs, and the output is given to an existing compiler to produce highly efficient differentiated code. Our implementation is a new AD tool for the Julia language, called Zygote, which presents high-level dynamic semantics while transparently compiling adjoint code under the hood. We discuss the benefits of this approach to both the usability and performance of AD tools.

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"
    - "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 "relay a high level ir for deep learning"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "glow graph lowering compiler techniques for neural networks"
    - "darts differentiable architecture search"
    - "neural ordinary differential equations"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "tangent automatic differentiation using source code transformation in python"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep learning with dynamic computation graphs"

Connected through hyperedge HE-3 (induced by source paper "backpack packing more into backprop"):
  with members:
    - "a modular approach to block diagonal hessian approximations for second order optimization methods"
    - "deepobs a deep learning optimizer benchmark suite"
    - "limitations of the empirical fisher approximation"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "not all samples are created equal deep learning with importance sampling"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "coupling adaptive batch sizes with learning rates"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "deployable probabilistic programming"):
  with members:
    - "probabilistic programming with densities in slicstan efficient flexible and deterministic"
    - "pyro deep universal probabilistic programming"
    - "composing modeling and inference operations with probabilistic program combinators"
    - "automatic differentiation in ml where we are and where we should be going"
    - "fashionable modelling with flux"
    - "tangent automatic differentiation using source code transformation for dynamically typed array programming"
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
    - "design and implementation of probabilistic programming language anglican"

Connected through hyperedge HE-5 (induced by source paper "a differentiable programming system to bridge machine learning and scientific computing"):
  with members:
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "automatic full compilation of julia programs and ml models to cloud tpus"
    - "exascale deep learning for climate analytics"
    - "neural ordinary differential equations"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "fashionable modelling with flux"
    - "a differentiable physics engine for deep learning in robotics"

Connected through hyperedge HE-6 (induced by source paper "tapirxla embedding fork join parallelism into the xla compiler in tensorflow using tapir"):
  with members:
    - "lstm benchmarks for deep learning frameworks"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "glow graph lowering compiler techniques for neural networks"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "imagenet training in minutes"
    - "neural collaborative filtering"
    - "dlvm a modern compiler infrastructure for deep learning systems"
    - "theano a python framework for fast computation of mathematical expressions"

Connected through hyperedge HE-7 (induced by source paper "tensorflow eager a multi stage python embedded dsl for machine learning"):
  with members:
    - "dlvm a modern compiler infrastructure for deep learning systems"
    - "deep residual learning for image recognition"
    - "automatic differentiation in machine learning a survey"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "segmental recurrent neural networks"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-8 (induced by source paper "automatic differentiation for adjoint stencil loops"):
  with members:
    - "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"
    - "automated derivation of the adjoint of high level transient finite element programs"

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)