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: "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
  Abstract: The vector representations of fixed dimensionality for words (in text) offered by Word2Vec have been shown to be very useful in many application scenarios, in particular due to the semantic information they carry. This paper proposes a parallel version, the Audio Word2Vec. It offers the vector representations of fixed dimensionality for variable-length audio segments. These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with very attractive real world applications such as query-by-example Spoken Term Detection (STD). In this STD application, the proposed approach significantly outperformed the conventional Dynamic Time Warping (DTW) based approaches at significantly lower computation requirements. We propose unsupervised learning of Audio Word2Vec from audio data without human annotation using Sequence-to-sequence Audoencoder (SA). SA consists of two RNNs equipped with Long Short-Term Memory (LSTM) units: the first RNN (encoder) maps the input audio sequence into a vector representation of fixed dimensionality, and the second RNN (decoder) maps the representation back to the input audio sequence. The two RNNs are jointly trained by minimizing the reconstruction error. Denoising Sequence-to-sequence Autoencoder (DSA) is furthered proposed offering more robust learning.

Connected through hyperedge HE-1 (induced by source paper "improving automated segmentation of radio shows with audio embeddings"):
  with members:
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "sound word2vec learning word representations grounded in sounds"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "soundnet learning sound representations from unlabeled video"
    - "very deep convolutional networks for large scale image recognition"
    - "advances in domain independent linear text segmentation"

Connected through hyperedge HE-2 (induced by source paper "representation learning for electronic health records"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "multimodal multitask representation learning for pathology biobank metadata prediction"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "clinical concept extraction for document level coding"
    - "graph convolutional transformer learning the graphical structure of electronic health records"
    - "unsupervised clinical language translation"
    - "publicly available clinical bert embeddings"

Connected through hyperedge HE-3 (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-4 (induced by source paper "an unsupervised autoregressive model for speech representation learning"):
  with members:
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "unspeech unsupervised speech context embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "dissecting contextual word embeddings architecture and representation"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-5 (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"
    - "multi view recurrent neural acoustic word embeddings"
    - "bag of tricks for efficient text classification"
    - "lattice based recurrent neural network encoders for neural machine translation"

Connected through hyperedge HE-6 (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-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 "from semi supervised to almost unsupervised speech recognition with very low resource by jointly learning phonetic structures from audio and text embeddings"):
  with members:
    - "completely unsupervised phoneme recognition by adversarially learning mapping relationships from audio embeddings"
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data"
    - "end to end neural segmental models for speech recognition"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "improved training of wasserstein gans"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"

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.SD(Sound)