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: "representations of language in a model of visually grounded speech signal"
  Abstract: We present a visually grounded model of speech perception which projects spoken utterances and images to a joint semantic space. We use a multi-layer recurrent highway network to model the temporal nature of spoken speech, and show that it learns to extract both form and meaning-based linguistic knowledge from the input signal. We carry out an in-depth analysis of the representations used by different components of the trained model and show that encoding of semantic aspects tends to become richer as we go up the hierarchy of layers, whereas encoding of form-related aspects of the language input tends to initially increase and then plateau or decrease.

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:
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed 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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "do cross modal systems leverage semantic relationships"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "voxceleb2 deep speaker recognition"
    - "learning cross modal deep embeddings for multi object image retrieval using text and sketch"
    - "learning two branch neural networks for image text matching tasks"
    - "dual path convolutional image text embedding with instance loss"
    - "visually aligned word embeddings for improving zero shot learning"
    - "look listen and learn"
    - "learning deep representations of medical images using siamese cnns with application to content based image retrieval"

Connected through hyperedge HE-4 (induced by source paper "learning semantic sentence representations from visually grounded language without lexical knowledge"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "using sparse semantic embeddings learned from multimodal text and image data to model human conceptual knowledge"
    - "predicting visual features from text for image and video caption retrieval"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "order embeddings and character level convolutions for multimodal alignment"
    - "learning visually grounded sentence representations"
    - "lstm a search space odyssey"

Connected through hyperedge HE-5 (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-6 (induced by source paper "large scale representation learning from visually grounded untranscribed speech"):
  with members:
    - "towards visually grounded sub word speech unit discovery"
    - "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"
    - "symbolic inductive bias for visually grounded learning of spoken language"
    - "benchmark analysis of representative deep neural network architectures"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "efficient natural language response suggestion for smart reply"

Connected through hyperedge HE-7 (induced by source paper "transfer learning from audio visual grounding to speech recognition"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "training asr models by generation of contextual information"):
  with members:
    - "transformers with convolutional context for asr"
    - "a comparative study on transformer vs rnn in speech applications"
    - "lessons from building acoustic models with a million hours of speech"
    - "wav2vec unsupervised pre training for speech recognition"
    - "roberta a robustly optimized bert pretraining approach"
    - "hierarchical neural story generation"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)