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: "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
  Abstract: Deep neural networks are among the most influential architectures of deep learning algorithms, being deployed in many mobile intelligent applications. End-side services, such as intelligent personal assistants (IPAs), autonomous cars, and smart home services often employ either simple local models or complex remote models on the cloud. Mobile-only and cloud-only computations are currently the status quo approaches. In this paper, we propose an efficient, adaptive, and practical engine, JointDNN, for collaborative computation between a mobile device and cloud for DNNs in both inference and training phase. JointDNN not only provides an energy and performance efficient method of querying DNNs for the mobile side, but also benefits the cloud server by reducing the amount of its workload and communications compared to the cloud-only approach. Given the DNN architecture, we investigate the efficiency of processing some layers on the mobile device and some layers on the cloud server. We provide optimization formulations at layer granularity for forward and backward propagation in DNNs, which can adapt to mobile battery limitations and cloud server load constraints and quality of service. JointDNN achieves up to 18X and 32X reductions on the latency and mobile energy consumption of querying DNNs, respectively.

Connected through hyperedge HE-1 (induced by source paper "run time deep model multiplexing"):
  with members:
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "deep feature compression for collaborative object detection"
    - "mnasnet platform aware neural architecture search for mobile"
    - "an analysis of deep neural network models for practical applications"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "efficient training of deep convolutional neural networks by augmentation in embedding space"):
  with members:
    - "a meta learning approach for custom model training"
    - "an analysis of deep neural network models for practical applications"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "a bayesian data augmentation approach for learning deep models"
    - "group equivariant convolutional networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"
    - "learning to see by moving"

Connected through hyperedge HE-3 (induced by source paper "back and forth prediction for deep tensor compression"):
  with members:
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "yolov3 an incremental improvement"
    - "deep feature compression for collaborative object detection"
    - "yolo9000 better faster stronger"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "edge ai on demand accelerating deep neural network inference via edge computing"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "zygarde time sensitive on device deep intelligence on intermittently powered systems"
    - "musical chair efficient real time recognition using collaborative iot devices"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "efficient resource allocation for on demand mobile edge cloud computing"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-5 (induced by source paper "bottlenet an end to end approach for feature compression in device edge co inference systems"):
  with members:
    - "improving device edge cooperative inference of deep learning via 2 step pruning"
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "deep feature compression for collaborative object detection"
    - "neural joint source channel coding"
    - "jalad joint accuracy and latency aware deep structure decoupling for edge cloud execution"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "improving device edge cooperative inference of deep learning via 2 step pruning"):
  with members:
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "channel pruning for accelerating very deep neural networks"
    - "pruning convolutional neural networks for resource efficient inference"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-7 (induced by source paper "bottlenet a deep learning architecture for intelligent mobile cloud computing services"):
  with members:
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "deep feature compression for collaborative object detection"
    - "intermediate deep feature compression the next battlefield of intelligent sensing"
    - "matching networks for one shot learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-8 (induced by source paper "towards collaborative intelligence friendly architectures for deep learning"):
  with members:
    - "deep feature compression for collaborative object detection"
    - "intermediate deep feature compression the next battlefield of intelligent sensing"
    - "matching networks for one shot learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "cudnn efficient primitives for deep learning"
    - "distributed representations of words and phrases and their compositionality"

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)