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: "multi view recurrent neural acoustic word embeddings"
  Abstract: Recent work has begun exploring neural acoustic word embeddings---fixed-dimensional vector representations of arbitrary-length speech segments corresponding to words. Such embeddings are applicable to speech retrieval and recognition tasks, where reasoning about whole words may make it possible to avoid ambiguous sub-word representations. The main idea is to map acoustic sequences to fixed-dimensional vectors such that examples of the same word are mapped to similar vectors, while different-word examples are mapped to very different vectors. In this work we take a multi-view approach to learning acoustic word embeddings, in which we jointly learn to embed acoustic sequences and their corresponding character sequences. We use deep bidirectional LSTM embedding models and multi-view contrastive losses. We study the effect of different loss variants, including fixed-margin and cost-sensitive losses. Our acoustic word embeddings improve over previous approaches for the task of word discrimination. We also present results on other tasks that are enabled by the multi-view approach, including cross-view word discrimination and word similarity.

Connected through hyperedge HE-1 (induced by source paper "effectiveness of self supervised pre training for speech recognition"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "acoustically grounded word embeddings for improved acoustics to word speech recognition"
    - "cloze driven pretraining of self attention networks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "an unsupervised autoregressive model for speech representation learning"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"

Connected through hyperedge HE-2 (induced by source paper "to tune or not to tune how about the best of both worlds"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "multi task deep neural networks for natural language understanding"
    - "how to fine tune bert for text classification"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "linguistic knowledge and transferability of contextual representations"
    - "deep visual domain adaptation a survey"

Connected through hyperedge HE-3 (induced by source paper "learned in speech recognition contextual acoustic word embeddings"):
  with members:
    - "advancing acoustic to word ctc model"
    - "deep contextualized word representations"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "learning acoustic word embeddings with temporal context for query by example speech search"
    - "how2 a large scale dataset for multimodal language understanding"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-4 (induced by source paper "confusion2vec towards enriching vector space word representations with representational ambiguities"):
  with members:
    - "morphological word embeddings"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "neural lattice language models"
    - "neural lattice to sequence models for uncertain inputs"
    - "enriching word vectors with subword information"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "bag of tricks for efficient text classification"
    - "lattice based recurrent neural network encoders for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "unsupervised end to end learning of discrete linguistic units for voice conversion"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "decoupled learning for conditional adversarial networks"
    - "improved audio embeddings by adjacency based clustering with applications in spoken term detection"
    - "voice impersonation using generative adversarial networks"
    - "unsupervised learning of semantic audio representations"
    - "tacotron towards end to end speech synthesis"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-6 (induced by source paper "on the contributions of visual and textual supervision in low resource semantic speech retrieval"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "towards visually grounded sub word speech unit discovery"
    - "symbolic inductive bias for visually grounded learning of spoken language"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "visually grounded cross lingual keyword spotting in speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "visually grounded learning of keyword prediction from untranscribed speech"

Connected through hyperedge HE-7 (induced by source paper "learning joint acoustic phonetic word embeddings"):
  with members:
    - "a survey of multi view representation learning"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "sampling matters in deep embedding learning"
    - "learning word like units from joint audio visual analysis"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "query by example search with discriminative neural acoustic word embeddings"
    - "learning word embeddings from speech"

Connected through hyperedge HE-8 (induced by source paper "acoustically grounded word embeddings for improved acoustics to word speech recognition"):
  with members:
    - "advancing acoustic to word ctc model"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "hierarchical multitask learning for ctc based speech recognition"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "end to end asr free keyword search from speech"

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)