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: "streaming small footprint keyword spotting using sequence to sequence models"
  Abstract: We develop streaming keyword spotting systems using a recurrent neural network transducer (RNN-T) model: an all-neural, end-to-end trained, sequence-to-sequence model which jointly learns acoustic and language model components. Our models are trained to predict either phonemes or graphemes as subword units, thus allowing us to detect arbitrary keyword phrases, without any out-of-vocabulary words. In order to adapt the models to the requirements of keyword spotting, we propose a novel technique which biases the RNN-T system towards a specific keyword of interest. #R##N#Our systems are compared against a strong sequence-trained, connectionist temporal classification (CTC) based "keyword-filler" baseline, which is augmented with a separate phoneme language model. Overall, our RNN-T system with the proposed biasing technique significantly improves performance over the baseline system.

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 "rnn transducer with language bias for end to end mandarin english code switching speech recognition"):
  with members:
    - "joint speech recognition and speaker diarization via sequence transduction"
    - "on the end to end solution to mandarin english code switching speech recognition"
    - "study of semi supervised approaches to improving english mandarin code switching speech recognition"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "state of the art speech recognition with sequence to sequence models"
    - "challenges of computational processing of code switching"
    - "end to end attention based large vocabulary speech recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-3 (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"
    - "a cascade architecture for keyword spotting on mobile devices"
    - "small footprint keyword spotting using deep neural network and connectionist temporal classifier"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"

Connected through hyperedge HE-4 (induced by source paper "query by example on device keyword spotting"):
  with members:
    - "donut ctc based query by example keyword spotting"
    - "attention based end to end models for small footprint keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "meta learning for few shot keyword spotting"
    - "fast asr free and almost zero resource keyword spotting using dtw and cnns for humanitarian monitoring"
    - "end to end asr free keyword search from speech"
    - "trainable frontend for robust and far field keyword spotting"

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"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"
    - "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 "streaming end to end speech recognition for mobile devices"):
  with members:
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "in datacenter performance analysis of a tensor processing unit"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "state of the art speech recognition with sequence to sequence models"
    - "personalized speech recognition on mobile devices"
    - "on the efficient representation and execution of deep acoustic models"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "listen attend and spell"

Connected through hyperedge HE-7 (induced by source paper "end to end streaming keyword spotting"):
  with members:
    - "a cascade architecture for keyword spotting on mobile devices"
    - "small footprint keyword spotting using deep neural network and connectionist temporal classifier"
    - "feedforward sequential memory networks a new structure to learn long term dependency"

Connected through hyperedge HE-8 (induced by source paper "no need for a lexicon evaluating the value of the pronunciation lexica in end to end models"):
  with members:
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "attention based models for speech recognition"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)