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 "large scale representation learning from visually grounded untranscribed speech"):
  Abstract: Systems that can associate images with their spoken audio captions are an important step towards visually grounded language learning. We describe a scalable method to automatically generate diverse audio for image captioning datasets. This supports pretraining deep networks for encoding both audio and images, which we do via a dual encoder that learns to align latent representations from both modalities. We show that a masked margin softmax loss for such models is superior to the standard triplet loss. We fine-tune these models on the Flickr8k Audio Captions Corpus and obtain state-of-the-art results---improving recall in the top 10 from 29.6% to 49.5%. We also obtain human ratings on retrieval outputs to better assess the impact of incidentally matching image-caption pairs that were not associated in the data, finding that automatic evaluation substantially underestimates the quality of the retrieved results.

Includes members:
  Member "towards visually grounded sub word speech unit discovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "absent cross lingual sentence representation mapping with bidirectional gans")
  Member "symbolic inductive bias for visually grounded learning of spoken language":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "benchmark analysis of representative deep neural network architectures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "what is the state of neural network pruning")
  Member "jointly discovering visual objects and spoken words from raw sensory input":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "end to end learning of visual representations from uncurated instructional videos")
  Member "visually grounded learning of keyword prediction from untranscribed speech":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "revisiting unreasonable effectiveness of data in deep learning era":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "efficient natural language response suggestion for smart reply":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")

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.CV(Computer Vision and Pattern Recognition)