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: "mcscript2 0 a machine comprehension corpus focused on script events and participants"
  Abstract: We introduce MCScript2.0, a machine comprehension corpus for the end-to-end evaluation of script knowledge. MCScript2.0 contains approx. 20,000 questions on approx. 3,500 texts, crowdsourced based on a new collection process that results in challenging questions. Half of the questions cannot be answered from the reading texts, but require the use of commonsense and, in particular, script knowledge. We give a thorough analysis of our corpus and show that while the task is not challenging to humans, existing machine comprehension models fail to perform well on the data, even if they make use of a commonsense knowledge base. The dataset is available at this http URL

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"
    - "align mask and select a simple method for incorporating commonsense knowledge into language representation models"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "what does bert learn from multiple choice reading comprehension datasets"):
  with members:
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "albert a lite bert for self supervised learning of language representations"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "cracking the contextual commonsense code understanding commonsense reasoning aptitude of deep contextual representations"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "ernie enhanced language representation with informative entities"
    - "do neural language representations learn physical commonsense"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "linguistic knowledge and transferability of contextual representations"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-4 (induced by source paper "detecting everyday scenarios in narrative texts"):
  with members:
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "inscript narrative texts annotated with script information"
    - "aligning script events with narrative texts"
    - "learning fine grained knowledge about contingent relations between everyday events"
    - "a database and lexicon of scripts for thoughttreasure"

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)