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: "acoustic and textual data augmentation for improved asr of code switching speech"
  Abstract: In this paper, we describe several techniques for improving the acoustic and language model of an automatic speech recognition (ASR) system operating on code-switching (CS) speech. We focus on the recognition of Frisian-Dutch radio broadcasts where one of the mixed languages, namely Frisian, is an under-resourced language. In previous work, we have proposed several automatic transcription strategies for CS speech to increase the amount of available training speech data. In this work, we explore how the acoustic modeling (AM) can benefit from monolingual speech data belonging to the high-resourced mixed language. For this purpose, we train state-of-the-art AMs, which were ineffective due to lack of training data, on a significantly increased amount of CS speech and monolingual Dutch speech. Moreover, we improve the language model (LM) by creating code-switching text, which is in practice almost non-existent, by (1) generating text using recurrent LMs trained on the transcriptions of the training CS speech data, (2) adding the transcriptions of the automatically transcribed CS speech data and (3) translating Dutch text extracted from the transcriptions of a large Dutch speech corpora. We report significantly improved CS ASR performance due to the increase in the acoustic and textual training data.

Connected through hyperedge HE-1 (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"
    - "espnet end to end speech processing toolkit"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"

Connected through hyperedge HE-2 (induced by source paper "constrained output embeddings for end to end code switching speech recognition with only monolingual data"):
  with members:
    - "learn to code switch data augmentation using copy mechanism on language modeling"
    - "espnet end to end speech processing toolkit"
    - "towards end to end code switching speech recognition"
    - "on the end to end solution to mandarin english code switching speech recognition"
    - "semi supervised acoustic model training for speech with code switching"
    - "code switching sentence generation by generative adversarial networks and its application to data augmentation"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "end to end attention based large vocabulary speech recognition"

Connected through hyperedge HE-3 (induced by source paper "end to end code switching asr for low resourced language pairs"):
  with members:
    - "multi graph decoding for code switching asr"
    - "a survey of code switched speech and language processing"
    - "building a unified code switching asr system for south african languages"
    - "advancing acoustic to word ctc model"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "towards language universal end to end speech recognition"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"

Connected through hyperedge HE-4 (induced by source paper "a survey of code switched speech and language processing"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "building a unified code switching asr system for south african languages"
    - "hindi english code switching speech corpus"
    - "a dataset for building code mixed goal oriented conversation systems"
    - "study of semi supervised approaches to improving english mandarin code switching speech recognition"
    - "joining hands exploiting monolingual treebanks for parsing of code mixing data"
    - "crowdsourcing universal part of speech tags for code switching"
    - "a multi task approach for named entity recognition in social media data"

Connected through hyperedge HE-5 (induced by source paper "training a code switching language model with monolingual data"):
  with members:
    - "acoustically grounded word embeddings for improved acoustics to word speech recognition"
    - "constrained output embeddings for end to end code switching speech recognition with only monolingual data"
    - "learn to code switch data augmentation using copy mechanism on language modeling"
    - "language modeling for code switching evaluation integration of monolingual data and discriminative training"
    - "code switching sentence generation by generative adversarial networks and its application to data augmentation"
    - "direct acoustics to word models for english conversational speech recognition"
    - "dual language models for code mixed speech recognition"

Connected through hyperedge HE-6 (induced by source paper "multi graph decoding for code switching asr"):
  with members:
    - "code switching detection with data augmented acoustic and language models"
    - "semi supervised acoustic model training for speech with code switching"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "finding consensus in speech recognition word error minimization and other applications of confusion networks"

Connected through hyperedge HE-7 (induced by source paper "large scale speaker diarization of radio broadcast archives"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "on the end to end solution to mandarin english code switching speech recognition"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "unsupervised and efficient vocabulary expansion for recurrent neural network language models in asr"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "improved training of end to end attention models for speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "unsupervised pretraining for sequence to sequence learning"
    - "neural machine translation of rare words with subword units"
    - "attention based 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)