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: "encoding of phonology in a recurrent neural model of grounded speech"
  Abstract: We study the representation and encoding of phonemes in a recurrent neural network model of grounded speech. We use a model which processes images and their spoken descriptions, and projects the visual and auditory representations into the same semantic space. We perform a number of analyses on how information about individual phonemes is encoded in the MFCC features extracted from the speech signal, and the activations of the layers of the model. Via experiments with phoneme decoding and phoneme discrimination we show that phoneme representations are most salient in the lower layers of the model, where low-level signals are processed at a fine-grained level, although a large amount of phonological information is retain at the top recurrent layer. We further find out that the attention mechanism following the top recurrent layer significantly attenuates encoding of phonology and makes the utterance embeddings much more invariant to synonymy. Moreover, a hierarchical clustering of phoneme representations learned by the network shows an organizational structure of phonemes similar to those proposed in linguistics.

Connected through hyperedge HE-1 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "what do rnn language models learn about filler gap dependencies"
    - "rule induction for global explanation of trained models"
    - "understanding convolutional neural networks for text classification"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"

Connected through hyperedge HE-2 (induced by source paper "tabula nearly rasa probing the linguistic knowledge of character level neural language models trained on unsegmented text"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "explaining character aware neural networks for word level prediction do they discover linguistic rules"
    - "assessing composition in sentence vector representations"
    - "an analysis of neural language modeling at multiple scales"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "recurrent neural networks in linguistic theory revisiting pinker and prince 1988 and the past tense debate"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-3 (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-4 (induced by source paper "correlating neural and symbolic representations of language"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "symbolic inductive bias for visually grounded learning of spoken language"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "deep contextualized word representations"
    - "how agents see things on visual representations in an emergent language game"
    - "analysis methods in neural language processing a survey"
    - "understanding learning dynamics of language models with svcca"
    - "limitations in learning an interpreted language with recurrent models"

Connected through hyperedge HE-5 (induced by source paper "evaluating the representational hub of language and vision models"):
  with members:
    - "defoiling foiled image captions"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "universal sentence encoder"
    - "how agents see things on visual representations in an emergent language game"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "guesswhat visual object discovery through multi modal dialogue"

Connected through hyperedge HE-6 (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"
    - "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"
    - "representations of language in a model of visually grounded speech signal"
    - "image pivoting for learning multilingual multimodal representations"

Connected through hyperedge HE-7 (induced by source paper "analyzing phonetic and graphemic representations in end to end automatic speech recognition"):
  with members:
    - "towards end to end code switching speech recognition"
    - "analyzing learned representations of a deep asr performance prediction model"
    - "stream attention based multi array end to end speech recognition"
    - "phonetic and graphemic systems for multi genre broadcast transcription"
    - "analysis methods in neural language processing a survey"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "learning word like units from joint audio visual analysis"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"

Connected through hyperedge HE-8 (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"
    - "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"
    - "representations of language in a model of visually grounded speech signal"

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)