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: "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
  Abstract: Reading comprehension has been widely studied. One of the most representative reading comprehension tasks is Stanford Question Answering Dataset (SQuAD), on which machine is already comparable with human. On the other hand, accessing large collections of multimedia or spoken content is much more difficult and time-consuming than plain text content for humans. It's therefore highly attractive to develop machines which can automatically understand spoken content. In this paper, we propose a new listening comprehension task - Spoken SQuAD. On the new task, we found that speech recognition errors have catastrophic impact on machine comprehension, and several approaches are proposed to mitigate the impact.

Connected through hyperedge HE-1 (induced by source paper "a pilot study on multiple choice machine reading comprehension for vietnamese texts"):
  with members:
    - "vietnamese transition based dependency parsing with supertag features"
    - "semeval 2017 task 3 community question answering"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "error analysis for vietnamese named entity recognition on deep neural network models"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "sberquad russian reading comprehension dataset description and analysis"
    - "lstm easy first dependency parsing with pre trained word embeddings and character level word embeddings in vietnamese"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "cross lingual transfer learning for question answering"):
  with members:
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual language model pretraining"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "multilingual extractive reading comprehension by runtime machine translation"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "speechbert cross modal pre trained language model for end to end spoken question answering"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "roberta a robustly optimized bert pretraining approach"
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "dream a challenge dataset and models for dialogue based reading comprehension"):
  with members:
    - "coqa a conversational question answering challenge"
    - "multi range reasoning for machine comprehension"
    - "odsqa open domain spoken question answering dataset"
    - "quac question answering in context"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "verb physics relative physical knowledge of actions and objects"
    - "the narrativeqa reading comprehension challenge"
    - "embracing data abundance booktest dataset for reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "mitigating the impact of speech recognition errors on spoken question answering by adversarial domain adaptation"):
  with members:
    - "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"
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-6 (induced by source paper "towards effective rebuttal listening comprehension using corpus wide claim mining"):
  with members:
    - "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"
    - "hierarchical attention model for improved machine comprehension of spoken content"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "investigating the effects of word substitution errors on sentence embeddings"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "representing sentences as low rank subspaces"
    - "enriching word vectors with subword information"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "odsqa open domain spoken question answering dataset"):
  with members:
    - "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"
    - "newsqa a machine comprehension dataset"

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)