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 "optimizing cnn model inference on cpus"):
  Abstract: The popularity of Convolutional Neural Network (CNN) models and the ubiquity of CPUs imply that better performance of CNN model inference on CPUs can deliver significant gain to a large number of users. To improve the performance of CNN inference on CPUs, current approaches like MXNet and Intel OpenVINO usually treat the model as a graph and use the high-performance libraries such as Intel MKL-DNN to implement the operations of the graph. While achieving reasonable performance on individual operations from the off-the-shelf libraries, this solution makes it inflexible to conduct optimizations at the graph level, as the local operation-level optimizations are predefined. Therefore, it is restrictive and misses the opportunity to optimize the end-to-end inference pipeline as a whole. This paper presents \emph{NeoCPU}, a comprehensive approach of CNN model inference on CPUs that employs a full-stack and systematic scheme of optimizations. \emph{NeoCPU} optimizes the operations as templates without relying on third-parties libraries, which enables further improvement of the performance via operation- and graph-level joint optimization. Experiments show that \emph{NeoCPU} achieves up to 3.45$\times$ lower latency for CNN model inference than the current state-of-the-art implementations on various kinds of popular CPUs.

Includes members:
  Member "tvm end to end optimization stack for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "torch struct deep structured prediction library")
  Member "glow graph lowering compiler techniques for neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "anatomy of high performance deep learning convolutions on simd architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high performance depthwise and pointwise convolutions on mobile devices")
  Member "learning to optimize tensor programs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "fft convolutions are faster than winograd on modern cpus here is why":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "band limited training and inference for convolutional neural networks")
  Member "intel ngraph an intermediate representation compiler and executor for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "tensor comprehensions framework agnostic high performance machine learning abstractions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "scheduling computation graphs of deep learning models on manycore cpus":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "high throughput cnn inference on embedded arm big little multi core processors")

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.DC(Distributed, Parallel, and Cluster Computing)