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: "rdeepsense reliable deep mobile computing models with uncertainty estimations"
  Abstract: Recent advances in deep learning have led various applications to unprecedented achievements, which could potentially bring higher intelligence to a broad spectrum of mobile and ubiquitous applications. Although existing studies have demonstrated the effectiveness and feasibility of running deep neural network inference operations on mobile and embedded devices, they overlooked the reliability of mobile computing models. Reliability measurements such as predictive uncertainty estimations are key factors for improving the decision accuracy and user experience. In this work, we propose RDeepSense, the first deep learning model that provides well-calibrated uncertainty estimations for resource-constrained mobile and embedded devices. RDeepSense enables the predictive uncertainty by adopting a tunable proper scoring rule as the training criterion and dropout as the implicit Bayesian approximation, which theoretically proves its correctness.To reduce the computational complexity, RDeepSense employs efficient dropout and predictive distribution estimation instead of model ensemble or sampling-based method for inference operations. We evaluate RDeepSense with four mobile sensing applications using Intel Edison devices. Results show that RDeepSense can reduce around 90% of the energy consumption while producing superior uncertainty estimations and preserving at least the same model accuracy compared with other state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "deep learning at the edge"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "pact parameterized clipping activation for quantized neural networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep learning for iot big data and streaming analytics a survey"
    - "wrpn wide reduced precision networks"
    - "hashnet deep learning to hash by continuation"
    - "distributed deep neural networks over the cloud the edge and end devices"

Connected through hyperedge HE-2 (induced by source paper "zygarde time sensitive on device deep intelligence on intermittently powered systems"):
  with members:
    - "alpaca intermittent execution without checkpoints"
    - "rethinking floating point for deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "adaptive neural networks for efficient inference"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "projectionnet learning efficient on device deep networks using neural projections"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-3 (induced by source paper "stfnets learning sensing signals from the time frequency perspective with short time fourier neural networks"):
  with members:
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "deep complex networks"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "semi supervised classification with graph convolutional networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-4 (induced by source paper "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal 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.LG(Machine Learning)