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: "demystifying differentiable programming shift reset the penultimate backpropagator"
  Abstract: Deep learning has seen tremendous success over the past decade in computer vision, machine translation, and gameplay. This success rests in crucial ways on gradient-descent optimization and the ability to learn parameters of a neural network by backpropagating observed errors. However, neural network architectures are growing increasingly sophisticated and diverse, which motivates an emerging quest for even more general forms of differentiable programming, where arbitrary parameterized computations can be trained by gradient descent. In this paper, we take a fresh look at automatic differentiation (AD) techniques, and especially aim to demystify the reverse-mode form of AD that generalizes backpropagation in neural networks. #R##N#We uncover a tight connection between reverse-mode AD and delimited continuations, which permits implementing reverse-mode AD purely via operator overloading and without any auxiliary data structures. We further show how this formulation of AD can be fruitfully combined with multi-stage programming (staging), leading to a highly efficient implementation that combines the performance benefits of deep learning frameworks based on explicit reified computation graphs (e.g., TensorFlow) with the expressiveness of pure library approaches (e.g., PyTorch).

Connected through hyperedge HE-1 (induced by source paper "a differential form pullback programming language for higher order reverse mode automatic differentiation"):
  with members:
    - "a simple differentiable programming language"
    - "backpropagation in the simply typed lambda calculus with linear negation"
    - "pytorch an imperative style high performance deep learning library"
    - "the simple essence of automatic differentiation"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow a system for large scale machine learning"
    - "automatic differentiation in machine learning a survey"
    - "a convenient differential category"

Connected through hyperedge HE-2 (induced by source paper "a simple differentiable programming language"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "efficient differentiable programming in a functional array processing language"
    - "dynamic control flow in large scale machine learning"
    - "the simple essence of automatic differentiation"
    - "tangent automatic differentiation using source code transformation for dynamically typed array programming"
    - "developing bug free machine learning systems with formal mathematics"
    - "tricks from deep learning"
    - "a convenient differential category"

Connected through hyperedge HE-3 (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"
    - "the simple essence of automatic differentiation"
    - "tensorflow a system for large scale machine learning"
    - "the stan math library reverse mode automatic differentiation in c"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "backpropagation in the simply typed lambda calculus with linear negation"):
  with members:
    - "the simple essence of automatic differentiation"
    - "proof nets and the linear substitution calculus"
    - "an introduction to differential linear logic proof nets models and antiderivatives"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "relay a high level ir for deep learning"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "don t unroll adjoint differentiating ssa form programs"
    - "glow graph lowering compiler techniques for neural networks"
    - "darts differentiable architecture search"
    - "neural ordinary differential equations"
    - "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-6 (induced by source paper "relay a high level compiler for deep learning"):
  with members:
    - "autograph imperative style coding with graph based performance"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "glow graph lowering compiler techniques for neural networks"
    - "a hardware software blueprint for flexible deep learning specialization"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "tangent automatic differentiation using source code transformation for dynamically typed array programming"

Connected through hyperedge HE-7 (induced by source paper "a differentiable programming system to bridge machine learning and scientific computing"):
  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"
    - "automatic full compilation of julia programs and ml models to cloud tpus"
    - "exascale deep learning for climate analytics"
    - "neural ordinary differential equations"
    - "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-8 (induced by source paper "autograph imperative style coding with graph based performance"):
  with members:
    - "automatic differentiation in ml where we are and where we should be going"
    - "tangent automatic differentiation using source code transformation in python"
    - "precise dynamic information flow for database backed applications"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow a system for large scale machine learning"
    - "improved semantic representations from tree structured long short term memory networks"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "julia a fast dynamic language for technical computing"

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.LG(Machine Learning)