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: "not all ops are created equal"
  Abstract: Efficient and compact neural network models are essential for enabling the deployment on mobile and embedded devices. In this work, we point out that typical design metrics for gauging the efficiency of neural network architectures -- total number of operations and parameters -- are not sufficient. These metrics may not accurately correlate with the actual deployment metrics such as energy and memory footprint. We show that throughput and energy varies by up to 5X across different neural network operation types on an off-the-shelf Arm Cortex-M7 microcontroller. Furthermore, we show that the memory required for activation data also need to be considered, apart from the model parameters, for network architecture exploration studies.

Connected through hyperedge HE-1 (induced by source paper "visual wake words dataset"):
  with members:
    - "sparse sparse architecture search for cnns on resource constrained microcontrollers"
    - "searching for mobilenetv3"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "rethinking machine learning development and deployment for edge devices"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "speech commands a dataset for limited vocabulary speech recognition"

Connected through hyperedge HE-2 (induced by source paper "netadapt platform aware neural network adaptation for mobile applications"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "pruning convolutional neural networks for resource efficient inference"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "rethinking machine learning development and deployment for edge devices"):
  with members:
    - "quantizing convolutional neural networks for low power high throughput inference engines"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "hello edge keyword spotting on microcontrollers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "bit fusion bit level dynamically composable architecture for accelerating deep neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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)