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 "words can shift dynamically adjusting word representations using nonverbal behaviors"):
  Abstract: Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication. Speaker intentions often vary dynamically depending on different nonverbal contexts, such as vocal patterns and facial expressions. As a result, when modeling human language, it is essential to not only consider the literal meaning of the words but also the nonverbal contexts in which these words appear. To better model human language, we first model expressive nonverbal representations by analyzing the fine-grained visual and acoustic patterns that occur during word segments. In addition, we seek to capture the dynamic nature of nonverbal intents by shifting word representations based on the accompanying nonverbal behaviors. To this end, we propose the Recurrent Attended Variation Embedding Network (RAVEN) that models the fine-grained structure of nonverbal subword sequences and dynamically shifts word representations based on nonverbal cues. Our proposed model achieves competitive performance on two publicly available datasets for multimodal sentiment analysis and emotion recognition. We also visualize the shifted word representations in different nonverbal contexts and summarize common patterns regarding multimodal variations of word representations.

Includes members:
  Member "multimodal language analysis with recurrent multistage fusion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on emergent communication in competitive multi agent teams")
  Member "multi attention recurrent network for human communication comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gated mechanism for attention based multimodal sentiment analysis")
  Member "multimodal affective analysis using hierarchical attention strategy with word level alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross modal learning for multi modal video categorization")
  Member "multimodal local global ranking fusion for emotion recognition":
    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 "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "found in translation learning robust joint representations by cyclic translations between modalities")
  Member "subword regularization improving neural network translation models with multiple subword candidates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fastai a layered api for deep learning")
  Member "probabilistic fasttext for multi sense word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "word sense disambiguation for 158 languages using word embeddings only")

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)