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: "argotario computational argumentation meets serious games"
  Abstract: An important skill in critical thinking and argumentation is the ability to spot and recognize fallacies. Fallacious arguments, omnipresent in argumentative discourse, can be deceptive, manipulative, or simply leading to `wrong moves' in a discussion. Despite their importance, argumentation scholars and NLP researchers with focus on argumentation quality have not yet investigated fallacies empirically. The nonexistence of resources dealing with fallacious argumentation calls for scalable approaches to data acquisition and annotation, for which the serious games methodology offers an appealing, yet unexplored, alternative. We present Argotario, a serious game that deals with fallacies in everyday argumentation. Argotario is a multilingual, open-source, platform-independent application with strong educational aspects, accessible at www.argotario.net.

Connected through hyperedge HE-1 (induced by source paper "findings of the nlp4if 2019 shared task on fine grained propaganda detection"):
  with members:
    - "fine tuned neural models for propaganda detection at the sentence and fragment levels"
    - "understanding bert performance in propaganda analysis"
    - "defending against neural fake news"
    - "neural architectures for fine grained propaganda detection in news"
    - "proppy a system to unmask propaganda in online news"
    - "fine grained analysis of propaganda in news articles"
    - "roberta a robustly optimized bert pretraining approach"
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"

Connected through hyperedge HE-2 (induced by source paper "neural architectures for fine grained propaganda detection in news"):
  with members:
    - "proppy a system to unmask propaganda in online news"
    - "fine grained analysis of propaganda in news articles"
    - "neural relation extraction within and across sentence boundaries"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "combining recurrent and convolutional neural networks for relation classification"

Connected through hyperedge HE-3 (induced by source paper "fine grained analysis of propaganda in news articles"):
  with members:
    - "proppy a system to unmask propaganda in online news"
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "before name calling dynamics and triggers of ad hominem fallacies in web argumentation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "introduction to the conll 2003 shared task language independent named entity recognition"
    - "introduction to the conll 2002 shared task language independent named entity recognition"

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)