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: "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
  Abstract: In this paper, we explore the learning of neural network embeddings for natural images and speech waveforms describing the content of those images. These embeddings are learned directly from the waveforms without the use of linguistic transcriptions or conventional speech recognition technology. While prior work has investigated this setting in the monolingual case using English speech data, this work represents the first effort to apply these techniques to languages beyond English. Using spoken captions collected in English and Hindi, we show that the same model architecture can be successfully applied to both languages. Further, we demonstrate that training a multilingual model simultaneously on both languages offers improved performance over the monolingual models. Finally, we show that these models are capable of performing semantic cross-lingual speech-to-speech retrieval.

Connected through hyperedge HE-1 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech"):
  with members:
    - "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks"
    - "models of visually grounded speech signal pay attention to nouns a bilingual experiment on english and japanese"
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "unsupervised end to end learning of discrete linguistic units for voice conversion"
    - "language learning using speech to image retrieval"
    - "towards visually grounded sub word speech unit discovery"
    - "the zero resource speech challenge 2019 tts without t"
    - "word recognition competition and activation in a model of visually grounded speech"

Connected through hyperedge HE-2 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech"):
  with members:
    - "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"
    - "visual features for context aware speech recognition"
    - "sequence to sequence models can directly translate foreign speech"
    - "learning word like units from joint audio visual analysis"
    - "learning modality invariant representations for speech and images"
    - "unsupervised neural and bayesian models for zero resource speech processing"

Connected through hyperedge HE-3 (induced by source paper "grounding object detections with transcriptions"):
  with members:
    - "toward self supervised object detection in unlabeled videos"
    - "learning to detect and retrieve objects from unlabeled videos"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "grounded video description"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "how2 a large scale dataset for multimodal language understanding"
    - "the sound of pixels"

Connected through hyperedge HE-4 (induced by source paper "mass a large and clean multilingual corpus of sentence aligned spoken utterances extracted from the bible"):
  with members:
    - "europarl st a multilingual corpus for speech translation of parliamentary debates"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
    - "massively multilingual neural machine translation"
    - "direct speech to speech translation with a sequence to sequence model"
    - "massively multilingual adversarial speech recognition"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "towards unsupervised speech to text translation"

Connected through hyperedge HE-5 (induced by source paper "models of visually grounded speech signal pay attention to nouns a bilingual experiment on english and japanese"):
  with members:
    - "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "learning word like units from joint audio visual analysis"
    - "speech coco 600k visually grounded spoken captions aligned to mscoco data set"
    - "encoding of phonology in a recurrent neural model of grounded speech"
    - "representations of language in a model of visually grounded speech signal"
    - "image pivoting for learning multilingual multimodal representations"

Connected through hyperedge HE-6 (induced by source paper "towards visually grounded sub word speech unit discovery"):
  with members:
    - "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"
    - "learning word like units from joint audio visual analysis"
    - "unsupervised learning of semantic audio representations"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "encoding of phonology in a recurrent neural model of grounded speech"
    - "representations of language in a model of visually grounded speech signal"

Connected through hyperedge HE-7 (induced by source paper "classifying topics in speech when all you have is crummy translations"):
  with members:
    - "visually grounded cross lingual keyword spotting in speech"
    - "fast asr free and almost zero resource keyword spotting using dtw and cnns for humanitarian monitoring"
    - "towards speech to text translation without speech recognition"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-8 (induced by source paper "lessons learned in multilingual grounded language learning"):
  with members:
    - "imagination improves multimodal translation"
    - "learning visually grounded sentence representations"
    - "multilingual multi modal embeddings for natural language processing"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning language representations for typology prediction"
    - "attention strategies for multi source sequence to sequence learning"
    - "massively multilingual neural grapheme to phoneme conversion"

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)