human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "pytorch an imperative style high performance deep learning library":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "efficient differentiable programming in a functional array processing language":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "demystifying differentiable programming shift reset the penultimate backpropagator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a differential form pullback programming language for higher order reverse mode automatic differentiation")
  Member "dynamic control flow in large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforced genetic algorithm learning for optimizing computation graphs")
  Member "the simple essence of automatic differentiation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a differential form pullback programming language for higher order reverse mode automatic differentiation")
  Member "tangent automatic differentiation using source code transformation for dynamically typed array programming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to control pdes with differentiable physics")
  Member "developing bug free machine learning systems with formal mathematics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards identifying and closing gaps in assurance of autonomous road vehicles a collection of technical notes part 1")
  Member "tricks from deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dynamic control flow in large scale machine learning")

Question: Please tell me which class the source paper 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)