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: "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
  Abstract: Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeepIoT, that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices. FastDeepIoT makes two key contributions. First, FastDeepIoT automatically learns an accurate and highly interpretable execution time model for deep neural networks on the target device. This is done without prior knowledge of either the hardware specifications or the detailed implementation of the used deep learning library. Second, FastDeepIoT informs a compression algorithm how to minimize execution time on the profiled device without impacting accuracy. We evaluate FastDeepIoT using three different sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus. FastDeepIoT further reduces the neural network execution time by 48% to 78% and energy consumption by 37% to 69% compared with the state-of-the-art compression algorithms.

Connected through hyperedge HE-1 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference"):
  with members:
    - "advio an authentic dataset for visual inertial odometry"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "lstm based zero velocity detection for robust inertial navigation"
    - "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "lstm a search space odyssey"
    - "ridi robust imu double integration"

Connected through hyperedge HE-2 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations"):
  with members:
    - "towards cooperative data rate prediction for future mobile and vehicular 6g networks"
    - "a reinforcement learning approach for efficient opportunistic vehicle to cloud data transfer"
    - "empirical analysis of client based network quality prediction in vehicular multi mno networks"
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "toward intelligent vehicular networks a machine learning framework"
    - "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"
    - "learning how to communicate in the internet of things finite resources and heterogeneity"

Connected through hyperedge HE-3 (induced by source paper "fastva deep learning video analytics through edge processing and npu in mobile"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "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"
    - "cnndroid gpu accelerated execution of trained deep convolutional neural networks on android"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (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"
    - "adaptive neural networks for efficient inference"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "rdeepsense reliable deep mobile computing models with uncertainty estimations"
    - "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-5 (induced by source paper "pruning deep neural networks architectures with evolution strategy"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "studying the plasticity in deep convolutional neural networks using random pruning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "person re identification by deep joint learning of multi loss classification"
    - "hierarchical representations for efficient architecture search"
    - "thinet a filter level pruning method for deep neural network compression"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-6 (induced by source paper "stfnets learning sensing signals from the time frequency perspective with short time fourier neural networks"):
  with members:
    - "deep complex networks"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "rdeepsense reliable deep mobile computing models with uncertainty estimations"
    - "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"

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)