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: "implicit argument prediction with event knowledge"
  Abstract: Implicit arguments are not syntactically connected to their predicates, and are therefore hard to extract. Previous work has used models with large numbers of features, evaluated on very small datasets. We propose to train models for implicit argument prediction on a simple cloze task, for which data can be generated automatically at scale. This allows us to use a neural model, which draws on narrative coherence and entity salience for predictions. We show that our model has superior performance on both synthetic and natural data.

Connected through hyperedge HE-1 (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"
    - "modeling semantic plausibility by injecting world 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-2 (induced by source paper "implicit argument prediction as reading comprehension"):
  with members:
    - "improving implicit semantic role labeling by predicting semantic frame arguments"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "squad 100 000 questions for machine comprehension of text"
    - "dynamic coattention networks for question answering"
    - "machine comprehension using match lstm and answer pointer"
    - "gated attention readers for text comprehension"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "automatic event salience identification"):
  with members:
    - "towards better text understanding and retrieval through kernel entity salience modeling"
    - "swat a system for detecting salient wikipedia entities in texts"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "using sentence level lstm language models for script inference"
    - "adam a method for stochastic optimization"
    - "probabilistic frame induction"
    - "distributed representations of words and phrases and their compositionality"
    - "tagme on the fly annotation of short text fragments by wikipedia entities"

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)