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: "unsupervised cross modal alignment of speech and text embedding spaces"
  Abstract: Recent research has shown that word embedding spaces learned from text corpora of different languages can be aligned without any parallel data supervision. Inspired by the success in unsupervised cross-lingual word embeddings, in this paper we target learning a cross-modal alignment between the embedding spaces of speech and text learned from corpora of their respective modalities in an unsupervised fashion. The proposed framework learns the individual speech and text embedding spaces, and attempts to align the two spaces via adversarial training, followed by a refinement procedure. We show how our framework could be used to perform spoken word classification and translation, and the results on these two tasks demonstrate that the performance of our unsupervised alignment approach is comparable to its supervised counterpart. Our framework is especially useful for developing automatic speech recognition (ASR) and speech-to-text translation systems for low- or zero-resource languages, which have little parallel audio-text data for training modern supervised ASR and speech-to-text translation models, but account for the majority of the languages spoken across the world.

Connected through hyperedge HE-1 (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-2 (induced by source paper "universal adversarial audio perturbations"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "universal adversarial perturbations for speech recognition systems"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "adversarial initialization when your network performs the way i want"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "unsupervised text style transfer using language models as discriminators"
    - "knockoff nets stealing functionality of black box models"

Connected through hyperedge HE-3 (induced by source paper "unsupervised speech representation learning using wavenet autoencoders"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "disentangled sequential autoencoder"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "unsupervised machine translation using monolingual corpora only"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-4 (induced by source paper "unsupervised clinical language translation"):
  with members:
    - "towards unsupervised speech to text translation"
    - "unsupervised statistical machine translation"
    - "mapping unparalleled clinical professional and consumer languages with embedding alignment"
    - "deep contextualized word representations"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "unsupervised multimodal representation learning across medical images and reports"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-5 (induced by source paper "speechbert cross modal pre trained language model for end to end spoken question answering"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "roberta a robustly optimized bert pretraining approach"
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "optimal unsupervised domain translation"):
  with members:
    - "latent translation crossing modalities by bridging generative models"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "video to video synthesis"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "the role of minimal complexity functions in unsupervised learning of semantic mappings"

Connected through hyperedge HE-7 (induced by source paper "completely unsupervised phoneme recognition by a generative adversarial network harmonized with iteratively refined hidden markov models"):
  with members:
    - "completely unsupervised phoneme recognition by adversarially learning mapping relationships from audio embeddings"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised machine translation using monolingual corpora only"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "unsupervised sequence classification using sequential output statistics"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "learning joint acoustic phonetic word embeddings"):
  with members:
    - "a survey of multi view representation learning"
    - "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"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"

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)