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: "symbolic inductive bias for visually grounded learning of spoken language"
  Abstract: A widespread approach to processing spoken language is to first automatically transcribe it into text. An alternative is to use an end-to-end approach: recent works have proposed to learn semantic embeddings of spoken language from images with spoken captions, without an intermediate transcription step. We propose to use multitask learning to exploit existing transcribed speech within the end-to-end setting. We describe a three-task architecture which combines the objectives of matching spoken captions with corresponding images, speech with text, and text with images. We show that the addition of the speech/text task leads to substantial performance improvements on image retrieval when compared to training the speech/image task in isolation. We conjecture that this is due to a strong inductive bias transcribed speech provides to the model, and offer supporting evidence for this.

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 "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"
    - "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"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"

Connected through hyperedge HE-3 (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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "higher order comparisons of sentence encoder representations"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "correlating neural and symbolic representations of language"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"
    - "the natural language decathlon multitask learning as question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "how agents see things on visual representations in an emergent language game"

Connected through hyperedge HE-5 (induced by source paper "on the contributions of visual and textual supervision in low resource semantic speech retrieval"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "towards visually grounded sub word speech unit discovery"
    - "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"

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)