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: "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"
  Abstract: Multimedia or spoken content presents more attractive information than plain text content, but it's 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 highly attractive to develop a machine which can automatically understand spoken content and summarize the key information for humans to browse over. In this endeavor, we propose a new task of machine comprehension of spoken content. We define the initial goal as the listening comprehension test of TOEFL, a challenging academic English examination for English learners whose native language is not English. We further propose an Attention-based Multi-hop Recurrent Neural Network (AMRNN) architecture for this task, achieving encouraging results in the initial tests. Initial results also have shown that word-level attention is probably more robust than sentence-level attention for this task with ASR errors.

Connected through hyperedge HE-1 (induced by source paper "mmm multi stage multi task learning for multi choice reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "dynamically fused graph network for multi hop reasoning"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-2 (induced by source paper "mitigating the impact of speech recognition errors on spoken question answering by adversarial domain adaptation"):
  with members:
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "odsqa open domain spoken question answering dataset"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "improved training of wasserstein gans"
    - "multi domain adversarial learning for slot filling in spoken language understanding"
    - "reading wikipedia to answer open domain questions"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-3 (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"
    - "parsing argumentation structures in persuasive essays"
    - "hierarchical attention model for improved machine comprehension of spoken content"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "improving machine reading comprehension with general reading strategies"):
  with members:
    - "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "generating wikipedia by summarizing long sequences"
    - "what makes reading comprehension questions easier"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "coqa a conversational question answering challenge"
    - "multi range reasoning for machine comprehension"
    - "hfl rc system at semeval 2018 task 11 hybrid multi aspects model for commonsense reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "speech2vec a sequence to sequence framework for learning word embeddings from speech"):
  with members:
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "learning word like units from joint audio visual analysis"
    - "supervised and unsupervised transfer learning for question answering"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "neural amr sequence to sequence models for parsing and generation"
    - "speech based visual question answering"
    - "character composition model with convolutional neural networks for dependency parsing on morphologically rich languages"
    - "learning word embeddings from speech"

Connected through hyperedge HE-6 (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-7 (induced by source paper "supervised transfer learning for product information question answering"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "question answering through transfer learning from large fine grained supervision data"
    - "aspect augmented adversarial networks for domain adaptation"
    - "supervised and unsupervised transfer learning for question answering"
    - "bilateral multi perspective matching for natural language sentences"
    - "squad 100 000 questions for machine comprehension of text"
    - "modeling ambiguity subjectivity and diverging viewpoints in opinion question answering systems"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-8 (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"
    - "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"
    - "hierarchical attention model for improved machine comprehension of spoken content"

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)