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: "modeling semantic plausibility by injecting world knowledge"
  Abstract: Distributional data tells us that a man can swallow candy, but not that a man can swallow a paintball, since this is never attested. However both are physically plausible events. This paper introduces the task of semantic plausibility: recognizing plausible but possibly novel events. We present a new crowdsourced dataset of semantic plausibility judgments of single events such as "man swallow paintball". Simple models based on distributional representations perform poorly on this task, despite doing well on selection preference, but injecting manually elicited knowledge about entity properties provides a substantial performance boost. Our error analysis shows that our new dataset is a great testbed for semantic plausibility models: more sophisticated knowledge representation and propagation could address many of the remaining errors.

Connected through hyperedge HE-1 (induced by source paper "can a gorilla ride a camel learning semantic plausibility from text"):
  with members:
    - "universal dependency parsing from scratch"
    - "solving hard coreference problems"
    - "sp 10k a large scale evaluation set for selectional preference acquisition"
    - "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"
    - "do neural language representations learn physical commonsense"
    - "probing neural network comprehension of natural language arguments"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "annotation artifacts in natural language inference data"

Connected through hyperedge HE-2 (induced by source paper "does it make sense and why a pilot study for sense making and explanation"):
  with members:
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "verb physics relative physical knowledge of actions and objects"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"

Connected through hyperedge HE-3 (induced by source paper "picking apart story salads"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "deep contextualized word representations"
    - "implicit argument prediction with event knowledge"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "event representations with tensor based compositions"
    - "a structured self attentive sentence embedding"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"

Connected through hyperedge HE-4 (induced by source paper "machine common sense concept paper"):
  with members:
    - "modeling naive psychology of characters in simple commonsense stories"
    - "probing physics knowledge using tools from developmental psychology"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "event2mind commonsense inference on events intents and reactions"
    - "commonsense mining as knowledge base completion a study on the impact of novelty"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "verb physics relative physical knowledge of actions and objects"
    - "simulating action dynamics with neural process networks"

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)