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: "semantic speech retrieval with a visually grounded model of untranscribed speech"
  Abstract: There is a growing interest in models that can learn from unlabelled speech paired with visual context. This setting is relevant for low-resource speech processing, robotics, and human language acquisition research. Here, we study how a visually grounded speech model, trained on images of scenes paired with spoken captions, captures aspects of semantics. We use an external image tagger to generate soft text labels from images, which serve as targets for a neural model that maps untranscribed speech to (semantic) keyword labels. We introduce a newly collected data set of human semantic relevance judgements and an associated task,  semantic speech retrieval , where the goal is to search for spoken utterances that are semantically relevant to a given text query. Without seeing any text, the model trained on parallel speech and images achieves a precision of almost 60% on its top ten semantic retrievals. Compared to a supervised model trained on transcriptions, our model matches human judgements better by some measures, especially in retrieving non-verbatim semantic matches. We perform an extensive analysis of the model and its resulting representations.

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"
    - "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"
    - "unsupervised speech representation learning using wavenet autoencoders"

Connected through hyperedge HE-2 (induced by source paper "transfer learning from audio visual grounding to speech recognition"):
  with members:
    - "towards visually grounded sub word speech unit discovery"
    - "an unsupervised autoregressive model for speech representation learning"
    - "training augmentation with adversarial examples for robust speech recognition"
    - "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
    - "deep contextualized word representations"
    - "unsupervised adaptation with interpretable disentangled representations for distant conversational speech recognition"
    - "unsupervised textual grounding linking words to image concepts"
    - "jointly discovering visual objects and spoken words from raw sensory input"

Connected through hyperedge HE-3 (induced by source paper "on the contributions of visual and textual supervision in low resource semantic speech retrieval"):
  with members:
    - "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"
    - "learning word like units from joint audio visual analysis"

Connected through hyperedge HE-4 (induced by source paper "kite automatic speech recognition for unmanned aerial vehicles"):
  with members:
    - "vision meets drones a challenge"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "using the output embedding to improve language models"
    - "musan a music speech and noise corpus"
    - "vqa visual question answering"

Connected through hyperedge HE-5 (induced by source paper "self supervised audio visual co segmentation"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual event localization in unconstrained videos"
    - "the sound of pixels"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "audio visual scene analysis with self supervised multisensory features"
    - "learning modality invariant representations for speech and images"
    - "look listen and learn"

Connected through hyperedge HE-6 (induced by source paper "semantic query by example speech search using visual grounding"):
  with members:
    - "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"
    - "low resource speech to text translation"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "visually grounded cross lingual keyword spotting in speech"
    - "speech2vec a sequence to sequence framework for learning word embeddings from 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"

Connected through hyperedge HE-7 (induced by source paper "word recognition competition and activation in a model of visually grounded speech"):
  with members:
    - "models of visually grounded speech signal pay attention to nouns a bilingual experiment on english and japanese"
    - "one pixel attack for fooling deep neural networks"
    - "language learning using speech to image retrieval"
    - "towards visually grounded sub word speech unit discovery"
    - "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"
    - "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-8 (induced by source paper "a computational model of early language acquisition from audiovisual experiences of young infants"):
  with members:
    - "computational and robotic models of early language development a review"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "the zero resource speech challenge 2017"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "language bootstrapping learning word meanings from perception action association"

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)