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: "learning word like units from joint audio visual analysis"
  Abstract: Given a collection of images and spoken audio captions, we present a method for discovering word-like acoustic units in the continuous speech signal and grounding them to semantically relevant image regions. For example, our model is able to detect spoken instances of the word 'lighthouse' within an utterance and associate them with image regions containing lighthouses. We do not use any form of conventional automatic speech recognition, nor do we use any text transcriptions or conventional linguistic annotations. Our model effectively implements a form of spoken language acquisition, in which the computer learns not only to recognize word categories by sound, but also to enrich the words it learns with semantics by grounding them in images.

Connected through hyperedge HE-1 (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 modality invariant representations for speech and images"
    - "unsupervised neural and bayesian models for zero resource speech processing"

Connected through hyperedge HE-2 (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-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "learning joint acoustic phonetic word embeddings"):
  with members:
    - "a survey of multi view representation learning"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "sampling matters in deep embedding learning"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "query by example search with discriminative neural acoustic word embeddings"
    - "learning word embeddings from speech"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"

Connected through hyperedge HE-5 (induced by source paper "learning to detect and retrieve objects from unlabeled videos"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "learning to separate object sounds by watching unlabeled video"
    - "cross modal embeddings for video and audio retrieval"
    - "how2 a large scale dataset for multimodal language understanding"
    - "the sound of pixels"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-6 (induced by source paper "semantic query by example speech search using visual grounding"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "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"

Connected through hyperedge HE-7 (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"
    - "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-8 (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"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

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)