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: "effective extensible programming unleashing julia on gpus"
  Abstract: GPUs and other accelerators are popular devices for accelerating compute-intensive, parallelizable applications. However, programming these devices is a difficult task. Writing efficient device code is challenging, and is typically done in a low-level programming language. High-level languages are rarely supported, or do not integrate with the rest of the high-level language ecosystem. To overcome this, we propose compiler infrastructure to efficiently add support for new hardware or environments to an existing programming language. We evaluate our approach by adding support for NVIDIA GPUs to the Julia programming language. By integrating with the existing compiler, we significantly lower the cost to implement and maintain the new compiler, and facilitate reuse of existing application code. Moreover, use of the high-level Julia programming language enables new and dynamic approaches for GPU programming. This greatly improves programmer productivity, while maintaining application performance similar to that of the official NVIDIA CUDA toolkit.

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"
    - "don t unroll adjoint differentiating ssa form programs"
    - "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 "dynamic automatic differentiation of gpu broadcast kernels"):
  with members:
    - "confusion of tagged perturbations in forward automatic differentiation of higher order functions"
    - "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"
    - "the simple essence of automatic differentiation"
    - "tangent automatic differentiation using source code transformation in python"
    - "forward mode automatic differentiation in julia"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "don t unroll adjoint differentiating ssa form programs"):
  with members:
    - "automatic full compilation of julia programs and ml models to cloud tpus"
    - "automatic differentiation in ml where we are and where we should be going"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tangent automatic differentiation using source code transformation in python"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "automatic differentiation in machine learning a survey"

Connected through hyperedge HE-4 (induced by source paper "fashionable modelling with flux"):
  with members:
    - "deep learning with dynamic computation graphs"
    - "forward mode automatic differentiation in julia"
    - "improved semantic representations from tree structured long short term memory networks"
    - "very deep convolutional networks for large scale image recognition"
    - "julia a fresh approach to numerical 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.PL(Programming Languages)