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: "espnet end to end speech processing toolkit"
  Abstract: This paper introduces a new open source platform for end-to-end speech processing named ESPnet. ESPnet mainly focuses on end-to-end automatic speech recognition (ASR), and adopts widely-used dynamic neural network toolkits, Chainer and PyTorch, as a main deep learning engine. ESPnet also follows the Kaldi ASR toolkit style for data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. This paper explains a major architecture of this software platform, several important functionalities, which differentiate ESPnet from other open source ASR toolkits, and experimental results with major ASR benchmarks.

Connected through hyperedge HE-1 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection"):
  with members:
    - "streaming end to end speech recognition for mobile devices"
    - "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-2 (induced by source paper "cgcnn complex gabor convolutional neural network on raw speech"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "end to end speech recognition from the raw waveform"
    - "improving speech recognition by revising gated recurrent units"
    - "deep complex networks"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "end to end phoneme sequence recognition using convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "espresso a fast end to end neural speech recognition toolkit"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "parallelizable stack long short term memory"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "when does label smoothing help"
    - "who needs words lexicon free speech recognition"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"

Connected through hyperedge HE-4 (induced by source paper "acoustic to word models with conversational context information"):
  with members:
    - "advancing acoustic to word ctc model"
    - "hierarchical multi task learning with ctc"
    - "dialog context aware end to end speech recognition"
    - "multi modal data augmentation for end to end asr"
    - "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"
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"

Connected through hyperedge HE-5 (induced by source paper "dfanet deep feature aggregation for real time semantic segmentation"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pyramid attention network for semantic segmentation"
    - "context encoding for semantic segmentation"
    - "real time semantic image segmentation via spatial sparsity"
    - "deep layer aggregation"
    - "squeeze and excitation networks"

Connected through hyperedge HE-6 (induced by source paper "cif continuous integrate and fire for end to end speech recognition"):
  with members:
    - "transformers with convolutional context for asr"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping"
    - "wav2letter the fastest open source speech recognition system"
    - "an online attention based model for speech recognition"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "improved training of end to end attention models for speech recognition"
    - "extending recurrent neural aligner for streaming end to end speech recognition in mandarin"

Connected through hyperedge HE-7 (induced by source paper "deep spiking neural networks for large vocabulary automatic speech recognition"):
  with members:
    - "a tandem learning rule for efficient and rapid inference on deep spiking neural networks"
    - "an efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks"
    - "deep learning in spiking neural networks"
    - "surrogate gradient learning in spiking neural networks"
    - "comprehensive snn compression using admm optimization and activity regularization"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"

Connected through hyperedge HE-8 (induced by source paper "espnet tts unified reproducible and integratable open source end to end text to speech toolkit"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "jvs corpus free japanese multi speaker voice corpus"
    - "libritts a corpus derived from librispeech for text to speech"
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "back translation style data augmentation for end to end asr"
    - "waveglow a flow based generative network for speech synthesis"
    - "close to human quality tts with transformer"
    - "forward attention in sequence to sequence acoustic modeling for speech synthesis"

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)