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 "speech sentiment analysis via pre trained features from end to end asr models"):
  Abstract: In this paper, we propose to use pre-trained features from end-to-end ASR models to solve the speech sentiment analysis problem as a down-stream task. We show that end-to-end ASR features, which integrate both acoustic and text information from speech, achieve promising results. We use RNN with self-attention as the sentiment classifier, which also provides an easy visualization through attention weights to help interpret model predictions. We use well benchmarked IEMOCAP dataset and a new large-scale sentiment analysis dataset SWBD-senti for evaluation. Our approach improves the-state-of-the-art accuracy on IEMOCAP from 66.6% to 71.7%, and achieves an accuracy of 70.10% on SWBD-senti with more than 49,500 utterances.

Includes members:
  Member "lingvo a modular and scalable framework for sequence to sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "incorporating end to end speech recognition models for sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "eda enriching emotional dialogue acts using an ensemble of neural annotators")
  Member "attention augmented end to end multi task learning for emotion prediction from speech":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "similarity of speech emotion in different languages revealed by a neural network with attention")
  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 "exploring architectures data and units for streaming end to end speech recognition with rnn transducer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "a structured self attentive sentence embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning for audio signal processing")

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)