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 end to end speech recognition for mobile devices"
  Abstract: End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recognizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories.

Connected through hyperedge HE-1 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition"):
  with members:
    - "q8bert quantized 8bit bert"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "a comparative study on transformer vs rnn in speech applications"
    - "language modeling with deep transformers"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "fully quantized transformer for improved translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-2 (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"
    - "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"
    - "sequence discriminative training for deep learning based acoustic keyword spotting"

Connected through hyperedge HE-3 (induced by source paper "performance oriented neural architecture search"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "quenn quantization engine for low power neural networks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "hello edge keyword spotting on microcontrollers"
    - "optimal dnn primitive selection with partitioned boolean quadratic programming"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "smash one shot model architecture search through hypernetworks"

Connected through hyperedge HE-4 (induced by source paper "scaling up online speech recognition using convnets"):
  with members:
    - "reducing transformer depth on demand with structured dropout"
    - "rnn t for latency controlled asr with improved beam search"
    - "a comparative study on transformer vs rnn in speech applications"
    - "end to end asr from supervised to semi supervised learning with modern architectures"
    - "transformer based acoustic modeling for hybrid speech recognition"
    - "fully convolutional speech recognition"
    - "efficient neural audio synthesis"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-5 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search"):
  with members:
    - "rnn t for latency controlled asr with improved beam search"
    - "vectorization of hypotheses and speech for faster beam search in encoder decoder based speech recognition"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "advancing acoustic to word ctc model"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-6 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "dialog context aware end to end speech recognition"
    - "exploring rnn transducer for chinese speech recognition"
    - "an online attention based model for speech recognition"
    - "audio visual speech recognition with a hybrid ctc attention architecture"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "improved training of end to end attention models for speech recognition"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"

Connected through hyperedge HE-7 (induced by source paper "randomnet towards fully automatic neural architecture design for multimodal learning"):
  with members:
    - "mfas multimodal fusion architecture search"
    - "best practices for scientific research on neural architecture search"
    - "centralnet a multilayer approach for multimodal fusion"
    - "efficient neural architecture search via parameter sharing"
    - "learning transferable architectures for scalable image recognition"
    - "efficient architecture search by network transformation"
    - "truly multi modal youtube 8m video classification with video audio and text"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning to detect keyword parts and whole by smoothed max pooling"):
  with members:
    - "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"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"

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)