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: "towards collaborative intelligence friendly architectures for deep learning"
  Abstract: Modern mobile devices are equipped with high-performance hardware resources such as graphics processing units (GPUs), making the end-side intelligent services more feasible. Even recently, specialized silicons as neural engines are being used for mobile devices. However, most mobile devices are still not capable of performing real-time inference using very deep models. Computations associated with deep models for today's intelligent applications are typically performed solely on the cloud. This cloud-only approach requires significant amounts of raw data to be uploaded to the cloud over the mobile wireless network and imposes considerable computational and communication load on the cloud server. Recent studies have shown that the latency and energy consumption of deep neural networks in mobile applications can be notably reduced by splitting the workload between the mobile device and the cloud. In this approach, referred to as collaborative intelligence, intermediate features computed on the mobile device are offloaded to the cloud instead of the raw input data of the network, reducing the size of the data needed to be sent to the cloud. In this paper, we design a new collaborative intelligence friendly architecture by introducing a unit responsible for reducing the size of the feature data needed to be offloaded to the cloud to a greater extent, where this unit is placed after a selected layer of a deep model. Our proposed method, across different wireless networks, achieves on average 53x improvements for end-to-end latency and 68x improvements for mobile energy consumption compared to the status quo cloud-only approach for ResNet-50, while the accuracy loss is less than 2%.

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"
    - "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 "bit allocation for multi task collaborative intelligence"):
  with members:
    - "multi task learning with compressible features for collaborative intelligence"
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "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-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"
    - "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-4 (induced by source paper "bottlenet a deep learning architecture for intelligent mobile cloud computing services"):
  with members:
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "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"

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)