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 "found in translation learning robust joint representations by cyclic translations between modalities"):
  Abstract: Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities. However, existing work learns joint representations by requiring all modalities as input and as a result, the learned representations may be sensitive to noisy or missing modalities at test time. With the recent success of sequence to sequence (Seq2Seq) models in machine translation, there is an opportunity to explore new ways of learning joint representations that may not require all input modalities at test time. In this paper, we propose a method to learn robust joint representations by translating between modalities. Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input. We augment modality translations with a cycle consistency loss to ensure that our joint representations retain maximal information from all modalities. Once our translation model is trained with paired multimodal data, we only need data from the source modality at test time for final sentiment prediction. This ensures that our model remains robust from perturbations or missing information in the other modalities. We train our model with a coupled translation-prediction objective and it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICT-MMMO, and YouTube. Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to missing or perturbed modalities.

Includes members:
  Member "joint robust voicing detection and pitch estimation based on residual harmonics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "factorized multimodal transformer for multimodal sequential learning")
  Member "detection of glottal closure instants from speech signals a quantitative review":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "factorized multimodal transformer for multimodal sequential learning")
  Member "multimodal language analysis with recurrent multistage fusion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on emergent communication in competitive multi agent teams")
  Member "multimodal sentiment analysis with word level fusion and reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gated mechanism for attention based multimodal sentiment analysis")
  Member "multimodal local global ranking fusion for emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning factorized multimodal representations")
  Member "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning factorized multimodal representations")
  Member "phrase based neural unsupervised machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "learning factorized multimodal representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion")

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.LG(Machine Learning)