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: "towards unsupervised automatic speech recognition trained by unaligned speech and text only"
  Abstract: Automatic speech recognition (ASR) has been widely researched with supervised approaches, while many low-resourced languages lack audio-text aligned data, and supervised methods cannot be applied on them. #R##N#In this work, we propose a framework to achieve unsupervised ASR on a read English speech dataset, where audio and text are unaligned. In the first stage, each word-level audio segment in the utterances is represented by a vector representation extracted by a sequence-of-sequence autoencoder, in which phonetic information and speaker information are disentangled. #R##N#Secondly, semantic embeddings of audio segments are trained from the vector representations using a skip-gram model. Last but not the least, an unsupervised method is utilized to transform semantic embeddings of audio segments to text embedding space, and finally the transformed embeddings are mapped to words. #R##N#With the above framework, we are towards unsupervised ASR trained by unaligned text and speech only.

Connected through hyperedge HE-1 (induced by source paper "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "an iterative closest point method for unsupervised word translation"
    - "non adversarial unsupervised word translation"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "unsupervised learning of semantic audio representations"
    - "end to end neural segmental models for speech recognition"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"

Connected through hyperedge HE-2 (induced by source paper "unsupervised cross modal alignment of speech and text embedding spaces"):
  with members:
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised machine translation using monolingual corpora only"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"
    - "word translation without parallel data"

Connected through hyperedge HE-3 (induced by source paper "improved audio embeddings by adjacency based clustering with applications in spoken term detection"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "learning acoustic word embeddings with temporal context for query by example speech search"
    - "unsupervised learning of semantic audio representations"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "multi view recurrent neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "deep convolutional acoustic word embeddings using word pair side information"

Connected through hyperedge HE-4 (induced by source paper "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data"):
  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"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "non adversarial unsupervised word translation"
    - "query by example search with discriminative neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"

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)