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: "align mask and select a simple method for incorporating commonsense knowledge into language representation models"
  Abstract: Neural language representation models such as Bidirectional Encoder Representations from Transformers (BERT) pre-trained on large-scale corpora can well capture rich semantics from plain text, and can be fine-tuned to consistently improve the performance on various natural language processing (NLP) tasks. However, the existing pre-trained language representation models rarely consider explicitly incorporating commonsense knowledge or other knowledge. In this paper, we develop a pre-training approach for incorporating commonsense knowledge into language representation models. We construct a commonsense-related multi-choice question answering dataset for pre-training a neural language representation model. The dataset is created automatically by our proposed "align, mask, and select" (AMS) method. We also investigate different pre-training tasks. Experimental results demonstrate that pre-training models using the proposed approach followed by fine-tuning achieves significant improvements on various commonsense-related tasks, such as CommonsenseQA and Winograd Schema Challenge, while maintaining comparable performance on other NLP tasks, such as sentence classification and natural language inference (NLI) tasks, compared to the original BERT models.

Connected through hyperedge HE-1 (induced by source paper "graph based reasoning over heterogeneous external knowledge for commonsense question answering"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "mcscript2 0 a machine comprehension corpus focused on script events and participants"

Connected through hyperedge HE-2 (induced by source paper "teaching pretrained models with commonsense reasoning a preliminary kb based approach"):
  with members:
    - "incorporating external knowledge into machine reading for generative question answering"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cosmos qa machine reading comprehension with contextual commonsense reasoning"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "improving question answering by commonsense based pre training"
    - "a simple method for commonsense reasoning"

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)