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: "deep residual learning for small footprint keyword spotting"
  Abstract: We explore the application of deep residual learning and dilated convolutions to the keyword spotting task, using the recently-released Google Speech Commands Dataset as our benchmark. Our best residual network (ResNet) implementation significantly outperforms Google's previous convolutional neural networks in terms of accuracy. By varying model depth and width, we can achieve compact models that also outperform previous small-footprint variants. To our knowledge, we are the first to examine these approaches for keyword spotting, and our results establish an open-source state-of-the-art reference to support the development of future speech-based interfaces.

Connected through hyperedge HE-1 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks"):
  with members:
    - "an optimized recurrent unit for ultra low power keyword spotting"
    - "streaming end to end speech recognition for mobile devices"
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic grammar augmentation for robust voice command recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"

Connected through hyperedge HE-2 (induced by source paper "algorithm dependent generalization bounds for overparameterized deep residual networks"):
  with members:
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "temporal convolution for real time keyword spotting on mobile devices"
    - "training over parameterized deep resnet is almost as easy as training a two layer network"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "analysis of the gradient descent algorithm for a deep neural network model with skip connections"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"

Connected through hyperedge HE-3 (induced by source paper "fq conv fully quantized convolution for efficient and accurate inference"):
  with members:
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "deep neural network compression with single and multiple level quantization"
    - "label refinery improving imagenet classification through label progression"
    - "the high dimensional geometry of binary neural networks"
    - "hello edge keyword spotting on microcontrollers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "how can we be so dense the benefits of using highly sparse representations"):
  with members:
    - "sparse dnns with improved adversarial robustness"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "the elephant in the room"
    - "sparse unsupervised capsules generalize better"
    - "pruning filters for efficient convnets"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "predicting detection filters for small footprint open vocabulary keyword spotting"):
  with members:
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic grammar augmentation for robust voice command recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "hello edge keyword spotting on microcontrollers"

Connected through hyperedge HE-6 (induced by source paper "temporal convolution for real time keyword spotting on mobile devices"):
  with members:
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "mnasnet platform aware neural architecture search for mobile"
    - "hello edge keyword spotting on microcontrollers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "small footprint keyword spotting using deep neural network and connectionist temporal classifier"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-7 (induced by source paper "small footprint keyword spotting with graph convolutional network"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "dual attention network for scene segmentation"
    - "non local neural networks"
    - "hello edge keyword spotting on microcontrollers"
    - "neural message passing for quantum chemistry"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"
    - "semi supervised classification with graph convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "a monaural speech enhancement method for robust small footprint keyword spotting"):
  with members:
    - "attention based end to end models for small footprint keyword spotting"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "supervised speech separation based on deep learning an overview"
    - "honk a pytorch reimplementation of convolutional neural networks for keyword spotting"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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.CL(Computation and Language)