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 "multimodal language analysis with recurrent multistage fusion"):
  Abstract: Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities. Comprehending multimodal language requires modeling not only the interactions within each modality (intra-modal interactions) but more importantly the interactions between modalities (cross-modal interactions). In this paper, we propose the Recurrent Multistage Fusion Network (RMFN) which decomposes the fusion problem into multiple stages, each of them focused on a subset of multimodal signals for specialized, effective fusion. Cross-modal interactions are modeled using this multistage fusion approach which builds upon intermediate representations of previous stages. Temporal and intra-modal interactions are modeled by integrating our proposed fusion approach with a system of recurrent neural networks. The RMFN displays state-of-the-art performance in modeling human multimodal language across three public datasets relating to multimodal sentiment analysis, emotion recognition, and speaker traits recognition. We provide visualizations to show that each stage of fusion focuses on a different subset of multimodal signals, learning increasingly discriminative multimodal representations.

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 local global ranking fusion for emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "found in translation learning robust joint representations by cyclic translations between modalities")
  Member "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "found in translation learning robust joint representations by cyclic translations between modalities")
  Member "reusing neural speech representations for auditory emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "incorporating end to end speech recognition models for sentiment analysis")
  Member "learning factorized multimodal representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion")
  Member "visually grounded learning of keyword prediction from untranscribed speech":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "visual features for context aware speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "looking enhances listening recovering missing speech using images")

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)