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: "large scale cloze test dataset created by teachers"
  Abstract: Cloze tests are widely adopted in language exams to evaluate students' language proficiency. In this paper, we propose the first large-scale human-created cloze test dataset CLOTH, containing questions used in middle-school and high-school language exams. With missing blanks carefully created by teachers and candidate choices purposely designed to be nuanced, CLOTH requires a deeper language understanding and a wider attention span than previously automatically-generated cloze datasets. We test the performance of dedicatedly designed baseline models including a language model trained on the One Billion Word Corpus and show humans outperform them by a significant margin. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending the long-term context to be the key bottleneck.

Connected through hyperedge HE-1 (induced by source paper "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "multi task deep neural networks for natural language understanding"
    - "explicit bias discovery in visual question answering models"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "from recognition to cognition visual commonsense reasoning"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "gender bias in coreference resolution"

Connected through hyperedge HE-2 (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-3 (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"
    - "deep learning the indus script"
    - "gated word character recurrent language model"
    - "embracing data abundance booktest dataset for reading comprehension"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "chid a large scale chinese idiom dataset for cloze test"):
  with members:
    - "coqa a conversational question answering challenge"
    - "race large scale reading comprehension dataset from examinations"
    - "evaluating machine translation performance on chinese idioms with a blacklist method"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "the lambada dataset word prediction requiring a broad discourse context"
    - "consensus attention based neural networks for chinese reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "asking the right question inferring advice seeking intentions from personal narratives"):
  with members:
    - "modeling naive psychology of characters in simple commonsense stories"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "coqa a conversational question answering challenge"
    - "quac question answering in context"
    - "learning to ask neural question generation for reading comprehension"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "text summarization techniques a brief survey"

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)