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: "exploring ways to incorporate additional knowledge to improve natural language commonsense question answering"
  Abstract: DARPA and Allen AI have proposed a collection of datasets to encourage research in Question Answering domains where (commonsense) knowledge is expected to play an important role. Recent language models such as BERT and GPT that have been pre-trained on Wikipedia articles and books, have shown decent performance with little fine-tuning on several such Multiple Choice Question-Answering (MCQ) datasets. Our goal in this work is to develop methods to incorporate additional (commonsense) knowledge into language model based approaches for better question answering in such domains. In this work we first identify external knowledge sources, and show that the performance further improves when a set of facts retrieved through IR is prepended to each MCQ question during both training and test phase. We then explore if the performance can be further improved by providing task specific knowledge in different manners or by employing different strategies for using the available knowledge. We present three different modes of passing knowledge and five different models of using knowledge including the standard BERT MCQ model. We also propose a novel architecture to deal with situations where information to answer the MCQ question is scattered over multiple knowledge sentences. We take 200 predictions from each of our best models and analyze how often the given knowledge is useful, how many times the given knowledge is useful but system failed to use it and some other metrices to see the scope of further improvements.

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 "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"
    - "careful selection of knowledge to solve open book 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)