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: "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"
  Abstract: Abstract   Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which ’reverse engineering’ language development, i.e., building an effective system that mimics infant’s achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible. On the data side, accessible but privacy-preserving repositories of home data have to be setup. On the psycholinguistic side, specific tests have to be constructed to benchmark humans and machines at different linguistic levels. We discuss the feasibility of this approach and present an overview of current results.

Connected through hyperedge HE-1 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks"):
  with members:
    - "studying the inductive biases of rnns with synthetic variations of natural languages"
    - "a critical analysis of biased parsers in unsupervised parsing"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "tree to tree neural networks for program translation"
    - "rnns implicitly implement tensor product representations"

Connected through hyperedge HE-2 (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-3 (induced by source paper "unsupervised few shot learning via self supervised training"):
  with members:
    - "on first order meta learning algorithms"
    - "towards good practices on building effective cnn baseline model for person re identification"
    - "meta learning for semi supervised few shot classification"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "attention based few shot person re identification using meta learning"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-4 (induced by source paper "models of visually grounded speech signal pay attention to nouns a bilingual experiment on english and japanese"):
  with members:
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "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-5 (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"
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "one pixel attack for fooling deep neural networks"
    - "language learning using speech to image retrieval"
    - "towards visually grounded sub word speech unit discovery"
    - "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-6 (induced by source paper "computational and robotic models of early language development a review"):
  with members:
    - "computational theories of curiosity driven learning"
    - "kauffman s adjacent possible in word order evolution"

Connected through hyperedge HE-7 (induced by source paper "jointly discovering visual objects and spoken words from raw sensory input"):
  with members:
    - "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"
    - "cnn features are also great at unsupervised classification"
    - "look listen and learn"
    - "vse improving visual semantic embeddings with hard negatives"
    - "encoding of phonology in a recurrent neural model of grounded speech"

Connected through hyperedge HE-8 (induced by source paper "an embedded segmental k means model for unsupervised segmentation and clustering of speech"):
  with members:
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "end to end asr free keyword search from speech"
    - "unsupervised word segmentation and lexicon discovery using acoustic word embeddings"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "nonparametric bayesian double articulation analyzer for direct language acquisition from continuous speech signals"
    - "multi view recurrent neural acoustic word embeddings"
    - "deep convolutional acoustic word embeddings using word pair side information"
    - "revisiting k means new algorithms via bayesian nonparametrics"

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)