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: "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
  Abstract: Recent studies have shown the latency and energy consumption of deep neural networks can be significantly improved by splitting the network between the mobile device and cloud. This paper introduces a new deep learning architecture, called BottleNet, for reducing the feature size needed to be sent to the cloud. Furthermore, we propose a training method for compensating for the potential accuracy loss due to the lossy compression of features before transmitting them to the cloud. BottleNet achieves on average 30x improvement in end-to-end latency and 40x improvement in mobile energy consumption compared to the cloud-only approach with negligible accuracy loss.

Connected through hyperedge HE-1 (induced by source paper "run time deep model multiplexing"):
  with members:
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "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 "deep joint transmission recognition for power constrained iot devices"):
  with members:
    - "bottlenet an end to end approach for feature compression in device edge co inference systems"
    - "deep joint source channel coding for wireless image retrieval"
    - "improving device edge cooperative inference of deep learning via 2 step pruning"
    - "deepjscc f deep joint source channel coding of images with feedback"
    - "deep joint source channel coding for wireless image transmission"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "end to end optimized image compression"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-3 (induced by source paper "bit allocation for multi task collaborative intelligence"):
  with members:
    - "multi task learning with compressible features for collaborative intelligence"
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "yolov3 an incremental improvement"
    - "deep feature compression for collaborative object detection"
    - "ai benchmark running deep neural networks on android smartphones"
    - "multi task learning as multi objective optimization"
    - "intermediate deep feature compression the next battlefield of intelligent sensing"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "back and forth prediction for deep tensor compression"):
  with members:
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "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-5 (induced by source paper "transformative effects of iot blockchain and artificial intelligence on cloud computing evolution vision trends and open challenges"):
  with members:
    - "healthfog an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments"
    - "a deep learning based approach to vm behavior identification in cloud systems"
    - "edgelens deep learning based object detection in integrated iot fog and cloud computing environments"
    - "cloud chaser real time deep learning computer vision on low computing power devices"
    - "a survey on the security of blockchain systems"
    - "container based cluster orchestration systems a taxonomy and future directions"
    - "sustainable cloud computing foundations and future directions"
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"

Connected through hyperedge HE-6 (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"
    - "jointdnn an efficient training and inference engine 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-7 (induced by source paper "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"):
  with members:
    - "efficient processing of deep neural networks a tutorial and survey"
    - "agile autonomous driving using end to end deep imitation learning"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "deep visual foresight for planning robot motion"
    - "image to image translation with conditional adversarial networks"
    - "deep residual learning for image recognition"
    - "deep speech scaling up end to end speech recognition"
    - "learning to generate chairs with 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)