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: "online keyword spotting with a character level recurrent neural network"
  Abstract: In this paper, we propose a context-aware keyword spotting model employing a character-level recurrent neural network (RNN) for spoken term detection in continuous speech. The RNN is end-to-end trained with connectionist temporal classification (CTC) to generate the probabilities of character and word-boundary labels. There is no need for the phonetic transcription, senone modeling, or system dictionary in training and testing. Also, keywords can easily be added and modified by editing the text based keyword list without retraining the RNN. Moreover, the unidirectional RNN processes an infinitely long input audio streams without pre-segmentation and keywords are detected with low-latency before the utterance is finished. Experimental results show that the proposed keyword spotter significantly outperforms the deep neural network (DNN) and hidden Markov model (HMM) based keyword-filler model even with less computations.

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 "attention based end to end models for small footprint keyword spotting"):
  with members:
    - "convolutional recurrent neural networks for small footprint keyword spotting"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"
    - "a neural attention model for abstractive sentence summarization"
    - "end to end attention based large vocabulary speech recognition"
    - "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-3 (induced by source paper "donut ctc based query by example keyword spotting"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "query by example search with discriminative neural acoustic word embeddings"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"

Connected through hyperedge HE-4 (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"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "convolutional recurrent neural networks for small footprint keyword spotting"):
  with members:
    - "trainable frontend for robust and far field keyword spotting"
    - "an end to end architecture for keyword spotting and voice activity detection"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-6 (induced by source paper "an end to end architecture for keyword spotting and voice activity detection"):
  with members:
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep speech scaling up end to end speech recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)