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: "characterizing the deep neural networks inference performance of mobile applications"
  Abstract: Today's mobile applications are increasingly leveraging deep neural networks to provide novel features, such as image and speech recognitions. To use a pre-trained deep neural network, mobile developers can either host it in a cloud server, referred to as cloud-based inference, or ship it with their mobile application, referred to as on-device inference. In this work, we investigate the inference performance of these two common approaches on both mobile devices and public clouds, using popular convolutional neural networks. Our measurement study suggests the need for both on-device and cloud-based inferences for supporting mobile applications. In particular, newer mobile devices is able to run mobile-optimized CNN models in reasonable time. However, for older mobile devices or to use more complex CNN models, mobile applications should opt in for cloud-based inference. We further demonstrate that variable network conditions can lead to poor cloud-based inference end-to-end time. To support efficient cloud-based inference, we propose a CNN model selection algorithm called CNNSelect that dynamically selects the most appropriate CNN model for each inference request, and adapts its selection to match different SLAs and execution time budgets that are caused by variable mobile environments. The key idea of CNNSelect is to make inference speed and accuracy trade-offs at runtime using a set of CNN models. We demonstrated that CNNSelect smoothly improves inference accuracy while maintaining SLA attainment in 88.5% more cases than a greedy baseline.

Connected through hyperedge HE-1 (induced by source paper "a note on latency variability of deep neural networks for mobile inference"):
  with members:
    - "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "learning structured sparsity in deep neural networks"
    - "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"
    - "pruning filters for efficient convnets"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient 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)