human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "hierarchical attention model for improved machine comprehension of spoken content"
  Abstract: Multimedia or spoken content presents more attractive information than plain text content, but the former is more difficult to display on a screen and be selected by a user. As a result, accessing large collections of the former is much more difficult and time-consuming than the latter for humans. It's therefore highly attractive to develop machines which can automatically understand spoken content and summarize the key information for humans to browse over. In this endeavor, a new task of machine comprehension of spoken content was proposed recently. The initial goal was defined as the listening comprehension test of TOEFL, a challenging academic English examination for English learners whose native languages are not English. An Attention-based Multi-hop Recurrent Neural Network (AMRNN) architecture was also proposed for this task, which considered only the sequential relationship within the speech utterances. In this paper, we propose a new Hierarchical Attention Model (HAM), which constructs multi-hopped attention mechanism over tree-structured rather than sequential representations for the utterances. Improved comprehension performance robust with respect to ASR errors were obtained.

Connected through hyperedge HE-1 (induced by source paper "towards effective rebuttal listening comprehension using corpus wide claim mining"):
  with members:
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "argumentation mining in user generated web discourse"
    - "a recorded debating dataset"
    - "joint learning of correlated sequence labelling tasks using bidirectional recurrent neural networks"
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"
    - "parsing argumentation structures in persuasive essays"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "odsqa open domain spoken question answering dataset"):
  with members:
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "drcd a chinese machine reading comprehension dataset"
    - "race large scale reading comprehension dataset from examinations"
    - "learning to paraphrase for question answering"
    - "supervised and unsupervised transfer learning for question answering"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"
    - "reading wikipedia to answer open domain questions"

Connected through hyperedge HE-3 (induced by source paper "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"):
  with members:
    - "reinforced mnemonic reader for machine comprehension"
    - "asr error management for improving spoken language understanding"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"
    - "reading wikipedia to answer open domain questions"
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"
    - "sequential convolutional neural networks for slot filling in spoken language understanding"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-4 (induced by source paper "transcribing lyrics from commercial song audio the first step towards singing content processing"):
  with members:
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"

Connected through hyperedge HE-5 (induced by source paper "supervised and unsupervised transfer learning for question answering"):
  with members:
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "aspect augmented adversarial networks for domain adaptation"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "neural domain adaptation for biomedical question answering"
    - "query based attention cnn for text similarity map"
    - "learning word embeddings from speech"
    - "newsqa a machine comprehension dataset"
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"

Question: Please tell me which class the center node 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)