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: "boda rtc productive generation of portable efficient code for convolutional neural networks on mobile computing platforms"
  Abstract: The popularity of neural networks (NNs) spans academia, industry, and popular culture. In particular, convolutional neural networks (CNNs) have been applied to many image based machine learning tasks and have yielded strong results. The availability of hardware/software systems for efficient training and deployment of large and/or deep CNN models has been, and continues to be, an important consideration for the field. Early systems for NN computation focused on leveraging existing dense linear algebra techniques and libraries. Current approaches use low-level machine specific programming and/or closed-source, purpose-built vendor libraries. In this work, we present an open source system that, compared to existing approaches, achieves competitive computational speed while achieving higher portability. We achieve this by targeting the vendor-neutral OpenCL platform using a code-generation approach. We argue that our approach allows for both: (1) the rapid development of new computational kernels for existing hardware targets, and (2) the rapid tuning of existing computational kernels for new hardware targets. Results are presented for a case study of targeting the Qualcomm Snapdragon 820 mobile computing platform for CNN deployment.

Connected through hyperedge HE-1 (induced by source paper "exploring the design space of deep convolutional neural networks at large scale"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "origami a 803 gop s w convolutional network accelerator"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "theano mpi a theano based distributed training framework"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "how to scale distributed deep learning"

Connected through hyperedge HE-2 (induced by source paper "a metaprogramming and autotuning framework for deploying deep learning applications"):
  with members:
    - "cltorch a hardware agnostic backend for the torch deep neural network library based on opencl"
    - "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.DC(Distributed, Parallel, and Cluster Computing)