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: "cltorch a hardware agnostic backend for the torch deep neural network library based on opencl"
  Abstract: This paper presents cltorch, a hardware-agnostic backend for the Torch neural network framework. cltorch enables training of deep neural networks on GPUs from diverse hardware vendors, including AMD, NVIDIA, and Intel. cltorch contains sufficient implementation to run models such as AlexNet, VGG, Overfeat, and GoogleNet. It is written using the OpenCL language, a portable compute language, governed by the Khronos Group. cltorch is the top-ranked hardware-agnostic machine learning framework on Chintala's convnet-benchmarks page. #R##N#This paper presents the technical challenges encountered whilst creating the cltorch backend for Torch, and looks in detail at the challenges related to obtaining a fast hardware-agnostic implementation. #R##N#The convolutional layers are identified as the key area of focus for accelerating hardware-agnostic frameworks. Possible approaches to accelerating the convolutional implementation are identified including: implementation of the convolutions using the implicitgemm or winograd algorithm, using a GEMM implementation adapted to the geometries associated with the convolutional algorithm, or using a pluggable hardware-specific convolutional implementation.

Connected through hyperedge HE-1 (induced by source paper "a metaprogramming and autotuning framework for deploying deep learning applications"):
  with members:
    - "boda rtc productive generation of portable efficient code for convolutional neural networks on mobile computing platforms"
    - "multi scale context aggregation by dilated convolutions"
    - "maxdnn an efficient convolution kernel for deep learning with maxwell gpus"
    - "efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems"
    - "fast algorithms for convolutional neural networks"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"
    - "deformable part models are convolutional neural networks"

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.NE(Neural and Evolutionary Computing)