human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "visually grounded learning of keyword prediction from untranscribed speech"):
  Abstract: During language acquisition, infants have the benefit of visual cues to ground spoken language. Robots similarly have access to audio and visual sensors. Recent work has shown that images and spoken captions can be mapped into a meaningful common space, allowing images to be retrieved using speech and vice versa. In this setting of images paired with untranscribed spoken captions, we consider whether computer vision systems can be used to obtain textual labels for the speech. Concretely, we use an image-to-words multi-label visual classifier to tag images with soft textual labels, and then train a neural network to map from the speech to these soft targets. We show that the resulting speech system is able to predict which words occur in an utterance---acting as a spoken bag-of-words classifier---without seeing any parallel speech and text. We find that the model often confuses semantically related words, e.g. "man" and "person", making it even more effective as a semantic keyword spotter.

Includes members:
  Member "visual features for context aware speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "looking enhances listening recovering missing speech using images")
  Member "learning word like units from joint audio visual analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech")
  Member "sound word2vec learning word representations grounded in sounds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving automated segmentation of radio shows with audio embeddings")
  Member "representations of language in a model of visually grounded speech signal":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "towards speech to text translation without speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "covost a diverse multilingual speech to text translation corpus")
  Member "unsupervised word segmentation and lexicon discovery using acoustic word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "ambient sound provides supervision for visual learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")
  Member "automatic description generation from images a survey of models datasets and evaluation measures":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multimodal matching transformer for live commenting")

Question: Please tell me which class the source paper 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)