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: "inscript narrative texts annotated with script information"
  Abstract: This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. Additionally, the text is annotated with coreference information. The corpus shows rich lexical variation and will serve as a unique resource for the study of the role of script knowledge in natural language processing.

Connected through hyperedge HE-1 (induced by source paper "mcscript2 0 a machine comprehension corpus focused on script events and participants"):
  with members:
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"
    - "record bridging the gap between human and machine commonsense reading comprehension"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "the narrativeqa reading comprehension challenge"
    - "aligning script events with narrative texts"

Connected through hyperedge HE-2 (induced by source paper "detecting everyday scenarios in narrative texts"):
  with members:
    - "mcscript2 0 a machine comprehension corpus focused on script events and participants"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "aligning script events with narrative texts"
    - "learning fine grained knowledge about contingent relations between everyday events"
    - "a database and lexicon of scripts for thoughttreasure"

Connected through hyperedge HE-3 (induced by source paper "mcscript a novel dataset for assessing machine comprehension using script knowledge"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "newsqa a machine comprehension dataset"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "squad 100 000 questions for machine comprehension of text"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "the goldilocks principle reading children s books with explicit memory representations"
    - "teaching machines to read and comprehend"
    - "learning semantic script knowledge with event embeddings"

Connected through hyperedge HE-4 (induced by source paper "temporal event knowledge acquisition via identifying narratives"):
  with members:
    - "a sequential model for classifying temporal relations between intra sentence events"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

Connected through hyperedge HE-5 (induced by source paper "an application of asp theories of intentions to understanding restaurant scenarios insights and narrative corpus"):
  with members:
    - "an asp methodology for understanding narratives about stereotypical activities"

Connected through hyperedge HE-6 (induced by source paper "modeling semantic expectation using script knowledge for referent prediction"):
  with members:
    - "solving hard coreference problems"
    - "compositional morphology for word representations and language modelling"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "aligning script events with narrative texts"):
  with members:
    - "neural semantic role labeling with dependency path embeddings"
    - "efficient estimation of word representations in vector space"

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)