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: "an experimental analysis of the power consumption of convolutional neural networks for keyword spotting"
  Abstract: Nearly all previous work on small-footprint keyword spotting with neural networks quantify model footprint in terms of the number of parameters and multiply operations for an inference pass. These values are, however, proxy measures since empirical performance in actual deployments is determined by many factors. In this paper, we study the power consumption of a family of convolutional neural networks for keyword spotting on a Raspberry Pi. We find that both proxies are good predictors of energy usage, although the number of multiplies is more predictive than the number of parameters. We also confirm that models with the highest accuracies are, unsurprisingly, the most power hungry.

Connected through hyperedge HE-1 (induced by source paper "keyword spotting for hearing assistive devices robust to external speakers"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "deep residual learning for small footprint keyword spotting"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "stochastic adaptive neural architecture search for keyword spotting"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "data driven sparse structure selection for deep neural networks"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "deep residual learning for small footprint keyword spotting"
    - "hello edge keyword spotting on microcontrollers"
    - "morphnet fast simple resource constrained structure learning of deep networks"

Connected through hyperedge HE-3 (induced by source paper "adaptive pruning of neural language models for mobile devices"):
  with members:
    - "fast parametric learning with activation memorization"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "an analysis of neural language modeling at multiple scales"
    - "an analysis of deep neural network models for practical applications"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "dynamic evaluation of neural sequence models"
    - "tying word vectors and word classifiers a loss framework for language modeling"

Connected through hyperedge HE-4 (induced by source paper "javascript convolutional neural networks for keyword spotting in the browser an experimental analysis"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "adaptive pruning of neural language models for mobile devices"
    - "deep residual learning for small footprint keyword spotting"
    - "hello edge keyword spotting on microcontrollers"
    - "convolutional recurrent neural networks for small footprint keyword spotting"
    - "pruning filters for efficient convnets"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "flops as a direct optimization objective for learning sparse neural networks"):
  with members:
    - "mnasnet platform aware neural architecture search for mobile"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "wide residual networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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.OH(Other Computer Science)