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 simple differentiable programming language"
  Abstract: Automatic differentiation plays a prominent role in scientific computing and in modern machine learning, often in the context of powerful programming systems. The relation of the various embodiments of automatic differentiation to the mathematical notion of derivative is not always entirely clear—discrepancies can arise, sometimes inadvertently.  In order to study automatic differentiation in such programming contexts, we define a small but expressive programming language that includes  a construct for reverse-mode differentiation. We give operational and denotational semantics for this language. The operational semantics employs popular implementation techniques, while the denotational semantics employs notions of differentiation familiar from real analysis. We establish that these semantics coincide.

Connected through hyperedge HE-1 (induced by source paper "a differential form pullback programming language for higher order reverse mode automatic differentiation"):
  with members:
    - "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"
    - "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 "on the versatility of open logical relations continuity automatic differentiation and a containment theorem"):
  with members:
    - "confusion of tagged perturbations in forward automatic differentiation of higher order functions"
    - "efficient differentiable programming in a functional array processing language"
    - "the simple essence of automatic differentiation"
    - "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"

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)