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: "careful selection of knowledge to solve open book question answering"
  Abstract: Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic. Recently a challenge involving such QA, OpenBookQA, has been proposed. Unlike most other NLQA tasks that focus on linguistic understanding, OpenBookQA requires deeper reasoning involving linguistic understanding as well as reasoning with common knowledge. In this paper we address QA with respect to the OpenBookQA dataset and combine state of the art language models with abductive information retrieval (IR), information gain based re-ranking, passage selection and weighted scoring to achieve 72.0% accuracy, an 11.6% improvement over the current state of the art.

Connected through hyperedge HE-1 (induced by source paper "natural language qa approaches using reasoning with external knowledge"):
  with members:
    - "transformers as soft reasoners over language"
    - "recent advances in natural language inference a survey of benchmarks resources and approaches"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "piqa reasoning about physical commonsense in natural language"
    - "language models as knowledge bases"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "understanding roles and entities datasets and models for natural language inference"
    - "qasc a dataset for question answering via sentence composition"

Connected through hyperedge HE-2 (induced by source paper "what s missing a knowledge gap guided approach for multi hop question answering"):
  with members:
    - "improving question answering with external knowledge"
    - "understanding dataset design choices for multi hop reasoning"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "neural models for reasoning over multiple mentions using coreference"

Connected through hyperedge HE-3 (induced by source paper "exploring ways to incorporate additional knowledge to improve natural language commonsense question answering"):
  with members:
    - "abductive commonsense reasoning"
    - "socialiqa commonsense reasoning about social interactions"
    - "roberta a robustly optimized bert pretraining approach"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wikihow a large scale text summarization dataset"

Connected through hyperedge HE-4 (induced by source paper "asu at textgraphs 2019 shared task explanation regeneration using language models and iterative re ranking"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "multi hop inference for sentence level textgraphs how challenging is meaningfully combining information for science question answering"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised textual grounding linking words to image concepts"
    - "race large scale reading comprehension dataset from examinations"

Connected through hyperedge HE-5 (induced by source paper "knowledge guided named entity recognition"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "scibert a pretrained language model for scientific text"
    - "question answering is a format when is it useful"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "exploring ways to incorporate additional knowledge to improve natural language commonsense question answering"
    - "the natural language decathlon multitask learning as question answering"

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)