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 "dynamic fusion for multimodal data"):
  Abstract: Effective fusion of data from multiple modalities, such as video, speech, and text, is challenging pertaining to the heterogeneous nature of multimodal data. In this paper, we propose dynamic fusion techniques that model context from different modalities efficiently. Instead of defining a deterministic fusion operation, such as concatenation, for the network, we let the network decide "how" to combine given multimodal features in the most optimal way. We propose two networks: 1) transfusion network, which learns to compress information from different modalities while preserving the context, and 2) a GAN-based network, which regularizes the learned latent space given context from complimenting modalities. A quantitative evaluation on the tasks of machine translation, and emotion recognition suggest that such adaptive networks are able to model context better than all existing methods.

Includes members:
  Member "multi lingual common semantic space construction via cluster consistent word embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "emu enhancing multilingual sentence embeddings with semantic specialization")
  Member "multimodal speech emotion recognition using audio and text":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interactive text to speech via semi supervised style transfer learning")
  Member "efficient low rank multimodal fusion with modality specific factors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "h owan multi distorted image restoration with tensor 1x1 convolution")
  Member "the memad submission to the wmt18 multimodal translation task":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on architectures for including visual information in neural language models for image description")
  Member "how2 a large scale dataset for multimodal language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "looking enhances listening recovering missing speech using images")
  Member "tensor fusion network for multimodal sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fgn fusion glyph network for chinese named entity recognition")
  Member "correlational neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards a general model of knowledge for facial analysis by multi source transfer learning")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)