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: "visually grounded cross lingual keyword spotting in speech"
  Abstract: Recent work considered how images paired with speech can be used as supervision for building speech systems when transcriptions are not available. We ask whether visual grounding can be used for cross-lingual keyword spotting: given a text keyword in one language, the task is to retrieve spoken utterances containing that keyword in another language. This could enable searching through speech in a low-resource language using text queries in a high-resource language. As a proof-of-concept, we use English speech with German queries: we use a German visual tagger to add keyword labels to each training image, and then train a neural network to map English speech to German keywords. Without seeing parallel speech-transcriptions or translations, the model achieves a precision at ten of 58%. We show that most erroneous retrievals contain equivalent or semantically relevant keywords; excluding these would improve P@10 to 91%.

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 "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"
    - "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"

Connected through hyperedge HE-3 (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"
    - "speech2vec a sequence to sequence framework for learning word embeddings from 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"

Connected through hyperedge HE-4 (induced by source paper "classifying topics in speech when all you have is crummy translations"):
  with members:
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "fast asr free and almost zero resource keyword spotting using dtw and cnns for humanitarian monitoring"
    - "towards speech to text translation without speech recognition"
    - "learning topic models going beyond svd"

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)