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: "embracing data abundance booktest dataset for reading comprehension"
  Abstract: There is a practically unlimited amount of natural language data available. Still, recent work in text comprehension has focused on datasets which are small relative to current computing possibilities. This article is making a case for the community to move to larger data and as a step in that direction it is proposing the BookTest, a new dataset similar to the popular Children's Book Test (CBT), however more than 60 times larger. We show that training on the new data improves the accuracy of our Attention-Sum Reader model on the original CBT test data by a much larger margin than many recent attempts to improve the model architecture. On one version of the dataset our ensemble even exceeds the human baseline provided by Facebook. We then show in our own human study that there is still space for further improvement.

Connected through hyperedge HE-1 (induced by source paper "investigating prior knowledge for challenging chinese machine reading comprehension"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "ernie enhanced representation through knowledge integration"
    - "geosqa a benchmark for scenario based question answering in the geography domain at high school level"
    - "pre training with whole word masking for chinese bert"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "chid a large scale chinese idiom dataset for cloze test"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "exploiting sentence embedding for medical question answering"

Connected through hyperedge HE-2 (induced by source paper "restoring ancient text using deep learning a case study on greek epigraphy"):
  with members:
    - "neural decipherment via minimum cost flow from ugaritic to linear b"
    - "effective character augmented word embedding for machine reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the narrativeqa reading comprehension challenge"
    - "large scale cloze test dataset created by teachers"
    - "deep learning the indus script"
    - "gated word character recurrent language model"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "harry potter and the action prediction challenge from natural language"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "contextualized word representations for reading comprehension"
    - "whodunnit crime drama as a case for natural language understanding"
    - "attention is all you need"
    - "the narrativeqa reading comprehension challenge"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-4 (induced by source paper "dream a challenge dataset and models for dialogue based reading comprehension"):
  with members:
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "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"

Connected through hyperedge HE-5 (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-6 (induced by source paper "simple and effective semi supervised question answering"):
  with members:
    - "deep contextualized word representations"
    - "neural models for key phrase detection and question generation"
    - "contextualized word representations for reading comprehension"
    - "two stage synthesis networks for transfer learning in machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "a unified query based generative model for question generation and question answering"
    - "supervised and unsupervised transfer learning for question answering"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-7 (induced by source paper "clicr a dataset of clinical case reports for machine reading comprehension"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "quasar datasets for question answering by search and reading"
    - "question answering from unstructured text by retrieval and comprehension"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-8 (induced by source paper "neural skill transfer from supervised language tasks to reading comprehension"):
  with members:
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "dissent sentence representation learning from explicit discourse relations"
    - "making neural qa as simple as possible but not simpler"
    - "reasonet learning to stop reading in machine comprehension"
    - "an overview of multi task learning in deep neural networks"

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)