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 simple essence of automatic differentiation"
  Abstract: Automatic differentiation (AD) in reverse mode (RAD) is a central component of deep learning and other uses of large-scale optimization. Commonly used RAD algorithms such as backpropagation, however, are complex and stateful, hindering deep understanding, improvement, and parallel execution. This paper develops a simple, generalized AD algorithm calculated from a simple, natural specification. The general algorithm is then specialized by varying the representation of derivatives. In particular, applying well-known constructions to a naive representation yields two RAD algorithms that are far simpler than previously known. In contrast to commonly used RAD implementations, the algorithms defined here involve no graphs, tapes, variables, partial derivatives, or mutation. They are inherently parallel-friendly, correct by construction, and usable directly from an existing programming language with no need for new data types or programming style, thanks to use of an AD-agnostic compiler plugin.

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"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "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"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "dynamic control flow in large scale machine learning"
    - "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 "on the versatility of open logical relations continuity automatic differentiation and a containment theorem"):
  with members:
    - "a simple differentiable programming language"
    - "confusion of tagged perturbations in forward automatic differentiation of higher order functions"
    - "efficient differentiable programming in a functional array processing language"
    - "automatic differentiation in machine learning a survey"
    - "step indexed logical relations for probability"
    - "abstract effects and proof relevant logical relations"
    - "church scott ptime an application of resource sensitive realizability"
    - "logical relations for monadic types"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "backpropagation in the simply typed lambda calculus with linear negation"):
  with members:
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "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-6 (induced by source paper "differentiable causal computations via delayed trace"):
  with members:
    - "guarded traced categories"
    - "diagrammatic semantics for digital circuits"
    - "stream differential equations specification formats and solution methods"
    - "infinite state energy games"
    - "rational streams coalgebraically"

Connected through hyperedge HE-7 (induced by source paper "dynamic automatic differentiation of gpu broadcast kernels"):
  with members:
    - "confusion of tagged perturbations in forward automatic differentiation of higher order functions"
    - "effective extensible programming unleashing julia on gpus"
    - "don t unroll adjoint differentiating ssa form programs"
    - "automatic full compilation of julia programs and ml models to cloud tpus"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "tangent automatic differentiation using source code transformation in python"
    - "forward mode automatic differentiation in julia"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "fixing incremental computation derivatives of fixpoints and the recursive semantics of datalog"):
  with members:
    - "change actions models of generalised differentiation"
    - "an introduction to differential linear logic proof nets models and antiderivatives"
    - "diffsharp an ad library for net languages"
    - "mackey complete spaces and power series a topological model of differential linear logic"
    - "automatic differentiation of algorithms for machine learning"

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)