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: "on device neural net inference with mobile gpus"
  Abstract: On-device inference of machine learning models for mobile phones is desirable due to its lower latency and increased privacy. Running such a compute-intensive task solely on the mobile CPU, however, can be difficult due to limited computing power, thermal constraints, and energy consumption. App developers and researchers have begun exploiting hardware accelerators to overcome these challenges. Recently, device manufacturers are adding neural processing units into high-end phones for on-device inference, but these account for only a small fraction of hand-held devices. In this paper, we present how we leverage the mobile GPU, a ubiquitous hardware accelerator on virtually every phone, to run inference of deep neural networks in real-time for both Android and iOS devices. By describing our architecture, we also discuss how to design networks that are mobile GPU-friendly. Our state-of-the-art mobile GPU inference engine is integrated into the open-source project TensorFlow Lite and publicly available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "efficient memory management for deep neural net inference"):
  with members:
    - "blazeface sub millisecond neural face detection on mobile gpus"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "profile guided memory optimization for deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "training deep nets with sublinear memory cost"
    - "deep residual learning for image recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-2 (induced by source paper "mind your weight s a large scale study on insufficient machine learning model protection in mobile apps"):
  with members:
    - "high fidelity extraction of neural network models"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "knockoff nets stealing functionality of black box models"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "a first look at deep learning apps on smartphones"
    - "privado practical and secure dnn inference"
    - "stealing hyperparameters in machine learning"

Connected through hyperedge HE-3 (induced by source paper "modeling of deep neural network dnn placement and inference in edge computing"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "a unified optimization approach for cnn model inference on integrated gpus"
    - "dynamic space time scheduling for gpu inference"
    - "infaas a model less inference serving system"
    - "balancing the migration of virtual network functions with replications in data centers"

Connected through hyperedge HE-4 (induced by source paper "ai benchmark all about deep learning on smartphones in 2019"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "semantic image synthesis with spatially adaptive normalization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "synthetic depth of field with a single camera mobile phone"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "ai benchmark running deep neural networks on android smartphones"
    - "relaxed quantization for discretized neural networks"

Connected through hyperedge HE-5 (induced by source paper "mlperf inference benchmark"):
  with members:
    - "ai benchmark all about deep learning on smartphones in 2019"
    - "mlperf training benchmark"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "benchmark analysis of representative deep neural network architectures"
    - "yolov3 an incremental improvement"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"
    - "tbd benchmarking and analyzing deep neural network training"

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)