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 "incorporating end to end speech recognition models for sentiment analysis"):
  Abstract: Previous work on emotion recognition demonstrated a synergistic effect of combining several modalities such as auditory, visual, and transcribed text to estimate the affective state of a speaker. Among these, the linguistic modality is crucial for the evaluation of an expressed emotion. However, manually transcribed spoken text cannot be given as input to a system practically. We argue that using ground-truth transcriptions during training and evaluation phases leads to a significant discrepancy in performance compared to real-world conditions, as the spoken text has to be recognized on the fly and can contain speech recognition mistakes. In this paper, we propose a method of integrating an automatic speech recognition (ASR) output with a character-level recurrent neural network for sentiment recognition. In addition, we conduct several experiments investigating sentiment recognition for human-robot interaction in a noise-realistic scenario which is challenging for the ASR systems. We quantify the improvement compared to using only the acoustic modality in sentiment recognition. We demonstrate the effectiveness of this approach on the Multimodal Corpus of Sentiment Intensity (MOSI) by achieving 73,6% accuracy in a binary sentiment classification task, exceeding previously reported results that use only acoustic input. In addition, we set a new state-of-the-art performance on the MOSI dataset (80.4% accuracy, 2% absolute improvement).

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 "multimodal sentiment analysis with word level fusion and reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gated mechanism for attention based multimodal sentiment analysis")
  Member "on the robustness of speech emotion recognition for human robot interaction with deep neural networks":
    also connected through other hyperedges:
      (none)
  Member "multi attention recurrent network for human communication comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "cross modal learning for multi modal video categorization")
  Member "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning robust and multilingual speech representations")
  Member "getting the subtext without the text scalable multimodal sentiment classification from visual and acoustic modalities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multimodal embeddings from language models")
  Member "reusing neural speech representations for auditory emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multimodal language analysis with recurrent multistage 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.CL(Computation and Language)