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 "automatic acceleration of numpy applications on gpus and multicore cpus"):
  Abstract: Frameworks like Numpy are a popular choice for application developers from varied fields such as image processing to bio-informatics to machine learning. Numpy is often used to develop prototypes or for deployment since it provides efficient implementation for operations involving arrays. Such an approach requires every operation to be executed eagerly. The result of each operation needs to be stored in memory which increases the memory footprint of the application. It also increases the bandwidth requirements since all uses must read from this memory. We propose an approach that records the sequence of Numpy operations for defered execution. When the values of an array are needed, for example when the values are stored to disk or displayed on screen, the sequence of operations required to compute these value are compiled into a function and executed. This removes the need to store/load intermediates in slow memory, resulting in better performance. In cases where the library implementation is more efficient (like matrix-matrix multiply), those are used instead. The approach also allows us to seamlessly target both multicore CPUs and NVIDIA GPUs, thereby porting the Numpy application to these architectures without changing the user program. The benefit of the approach is evaluated by targeting computation samples from various domains and on average on order of magnitude performance improvement over Numpy is observed.

Includes members:
  Member "theano a python framework for fast computation of mathematical expressions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "the numpy array a structure for efficient numerical computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")

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)