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: "cnndroid gpu accelerated execution of trained deep convolutional neural networks on android"
  Abstract: Many mobile applications running on smartphones and wearable devices would potentially benefit from the accuracy and scalability of deep CNN-based machine learning algorithms. However, performance and energy consumption limitations make the execution of such computationally intensive algorithms on mobile devices prohibitive. We present a GPU-accelerated library, dubbed CNNdroid [1], for execution of trained deep CNNs on Android-based mobile devices. Empirical evaluations show that CNNdroid achieves up to 60X speedup and 130X energy saving on current mobile devices. The CNNdroid open source library is available for download at https://github.com/ENCP/CNNdroid

Connected through hyperedge HE-1 (induced by source paper "fastva deep learning video analytics through edge processing and npu in mobile"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection"):
  with members:
    - "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000"
    - "transferable knowledge for low cost decision making in cloud environments"
    - "rectified decision trees towards interpretability compression and empirical soundness"
    - "the university of cambridge s machine translation systems for wmt18"
    - "multi representation ensembles and delayed sgd updates improve syntax based nmt"
    - "an analysis of deep neural network models for practical applications"
    - "fake it till you make it fishing for catfishes"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-3 (induced by source paper "mobisr efficient on device super resolution through heterogeneous mobile processors"):
  with members:
    - "embench quantifying performance variations of deep neural networks across modern commodity devices"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "deploying deep neural networks in the embedded space"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "fast and accurate single image super resolution via information distillation network"
    - "effnet an efficient structure for convolutional neural networks"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-4 (induced by source paper "characterizing the deep neural networks inference performance of mobile applications"):
  with members:
    - "no dnn left behind improving inference in the cloud with multi tenancy"
    - "deepcache principled cache for mobile deep vision"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "a first look at deep learning apps on smartphones"
    - "serving deep learning models in a serverless platform"
    - "learning transferable architectures for scalable image recognition"
    - "tensorflow serving flexible high performance ml serving"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-5 (induced by source paper "high throughput cnn inference on embedded arm big little multi core processors"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "tvm end to end optimization stack for deep learning"
    - "ai benchmark running deep neural networks on android smartphones"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "scheduling computation graphs of deep learning models on manycore cpus"
    - "optimizing cnn model inference on cpus"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"

Connected through hyperedge HE-6 (induced by source paper "phonebit efficient gpu accelerated binary neural network inference engine for mobile phones"):
  with members:
    - "hg caffe mobile and embedded neural network gpu opencl inference engine with fp16 supporting"
    - "ai benchmark running deep neural networks on android smartphones"
    - "learning efficient convolutional networks through network slimming"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "bmxnet an open source binary neural network implementation based on mxnet"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "hg caffe mobile and embedded neural network gpu opencl inference engine with fp16 supporting"):
  with members:
    - "ai benchmark running deep neural networks on android smartphones"
    - "mixed precision training"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "end to end learning for self driving cars"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "a first look at deep learning apps on smartphones"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "deepcache principled cache for mobile deep vision"
    - "digital watermarking for deep neural networks"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "securing input data of deep learning inference systems via partitioned enclave execution"
    - "understanding diverse usage patterns from large scale appstore service profiles"

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)