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: "pycuda and pyopencl a scripting based approach to gpu run time code generation"
  Abstract: High-performance computing has recently seen a surge of interest in heterogeneous systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices offer tremendous potential for performance and efficiency in important large-scale applications of computational science. However, exploiting this potential can be challenging, as one must adapt to the specialized and rapidly evolving computing environment currently exhibited by GPUs. One way of addressing this challenge is to embrace better techniques and develop tools tailored to their needs. This article presents one simple technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL, two open-source toolkits that supports this technique. In introducing PyCUDA and PyOpenCL, this article proposes the combination of a dynamic, high-level scripting language with the massive performance of a GPU as a compelling two-tiered computing platform, potentially offering significant performance and productivity advantages over conventional single-tier, static systems. The concept of RTCG is simple and easily implemented using existing, robust infrastructure. Nonetheless it is powerful enough to support (and encourage) the creation of custom application-specific tools by its users. The premise of the paper is illustrated by a wide range of examples where the technique has been applied with considerable success.

Connected through hyperedge HE-1 (induced by source paper "chainer a deep learning framework for accelerating the research cycle"):
  with members:
    - "yolov3 an incremental improvement"
    - "mesh tensorflow deep learning for supercomputers"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "imagenet training in minutes"
    - "squeeze and excitation networks"
    - "revisiting distributed synchronous sgd"
    - "pomegranate fast and flexible probabilistic modeling in python"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "towards understanding residual and dilated dense neural networks via convolutional sparse coding"):
  with members:
    - "on first order meta learning algorithms"
    - "densely connected convolutional networks for speech recognition"
    - "memory efficient implementation of densenets"
    - "meta sgd learning to learn quickly for few shot learning"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "position paper towards transparent machine learning"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "unexplainability and incomprehensibility of artificial intelligence"
    - "unpredictability of ai"
    - "a dual approach to scalable verification of deep networks"
    - "methods for interpreting and understanding deep neural networks"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "towards verified artificial intelligence"

Connected through hyperedge HE-4 (induced by source paper "gpu computing with python performance energy efficiency and usability"):
  with members:
    - "benchmarking opencl openacc openmp and cuda programming productivity performance and energy consumption"
    - "a comparative study of programming languages in rosetta code"
    - "best practices for scientific computing"
    - "a performance comparison of cuda and opencl"

Connected through hyperedge HE-5 (induced by source paper "cuda self organizing feature map based visual sentiment analysis of bank customer complaints for analytical crm"):
  with members:
    - "using self organizing maps for sentiment analysis"

Connected through hyperedge HE-6 (induced by source paper "deep learning for ontology reasoning"):
  with members:
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "can recursive neural tensor networks learn logical reasoning"

Connected through hyperedge HE-7 (induced by source paper "opencl actors adding data parallelism to actor based programming with caf"):
  with members:
    - "revisiting actor programming in c"

Connected through hyperedge HE-8 (induced by source paper "fireflies new software for interactively exploring dynamical systems using gpu computing"):
  with members:
    - "the numpy array a structure for efficient numerical computation"

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.DC(Distributed, Parallel, and Cluster Computing)