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: "max pooling loss training of long short term memory networks for small footprint keyword spotting"
  Abstract: We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements. The max-pooling loss training can be further guided by initializing with a cross-entropy loss trained network. A posterior smoothing based evaluation approach is employed to measure keyword spotting performance. Our experimental results show that LSTM models trained using cross-entropy loss or max-pooling loss outperform a cross-entropy loss trained baseline feed-forward Deep Neural Network (DNN). In addition, max-pooling loss trained LSTM with randomly initialized network performs better compared to cross-entropy loss trained LSTM. Finally, the max-pooling loss trained LSTM initialized with a cross-entropy pre-trained network shows the best performance, which yields 67:6% relative reduction compared to baseline feed-forward DNN in Area Under the Curve (AUC) measure.

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 "learning to detect keyword parts and whole by smoothed max pooling"):
  with members:
    - "streaming end to end speech recognition for mobile devices"
    - "end to end streaming keyword spotting"
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "a cascade architecture for keyword spotting on mobile devices"
    - "small footprint keyword spotting using deep neural network and connectionist temporal classifier"

Connected through hyperedge HE-3 (induced by source paper "ternary hybrid neural tree networks for highly constrained iot applications"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "hello edge keyword spotting on microcontrollers"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"
    - "morphnet fast simple resource constrained structure learning of deep networks"

Connected through hyperedge HE-4 (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"
    - "deep residual learning for small footprint keyword spotting"
    - "hello edge keyword spotting on microcontrollers"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "designing the next generation of intelligent personal robotic assistants for the physically impaired"):
  with members:
    - "mediapipe a framework for building perception pipelines"
    - "streaming end to end speech recognition for mobile devices"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "wavenet a generative model for raw audio"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-6 (induced by source paper "attention based end to end models for small footprint keyword spotting"):
  with members:
    - "convolutional recurrent neural networks for small footprint keyword spotting"
    - "a neural attention model for abstractive sentence summarization"
    - "end to end attention based large vocabulary speech recognition"
    - "online keyword spotting with a character level recurrent neural network"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "efficient keyword spotting using dilated convolutions and gating"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "deep residual learning for small footprint keyword spotting"
    - "hello edge keyword spotting on microcontrollers"
    - "wavenet a generative model for raw audio"
    - "musan a music speech and noise corpus"

Connected through hyperedge HE-8 (induced by source paper "streaming small footprint keyword spotting using sequence to sequence models"):
  with members:
    - "lstm a search space odyssey"
    - "optimizing expected word error rate via sampling for speech recognition"
    - "end to end asr free keyword search from speech"
    - "personalized speech recognition on mobile devices"
    - "on the efficient representation and execution of deep acoustic models"
    - "an end to end architecture for keyword spotting and voice activity detection"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "fast and accurate recurrent neural network acoustic models for speech 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.CL(Computation and Language)