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: "argumentation mining in user generated web discourse"
  Abstract: The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. i We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. ii We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. iii We create a new gold standard corpus 90k tokens in 340 documents and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.

Connected through hyperedge HE-1 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models"):
  with members:
    - "activity based analysis of open source software contributors roles and dynamics"
    - "analysis and detection of information types of open source software issue discussions"
    - "how do the open source communities address usability and ux issues an exploratory study"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "neural end to end learning for computational argumentation mining"
    - "easy over hard a case study on deep learning"
    - "parsing argumentation structures in persuasive essays"
    - "a computational approach to politeness with application to social factors"

Connected through hyperedge HE-2 (induced by source paper "a dataset independent set of baselines for relation prediction in argument mining"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "advances in pre training distributed word representations"
    - "argument mining with structured svms and rnns"
    - "autoencoder feature selector"
    - "parsing argumentation structures in persuasive essays"
    - "a decomposable attention model for natural language inference"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "multi task learning for argumentation mining"):
  with members:
    - "lstm a search space odyssey"
    - "neural end to end learning for computational argumentation mining"
    - "multi task learning for mental health using social media text"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "attention strategies for multi source sequence to sequence learning"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "cross lingual and cross domain discourse segmentation of entire documents"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"

Connected through hyperedge HE-4 (induced by source paper "tacam topic and context aware argument mining"):
  with members:
    - "robust argument unit recognition and classification"
    - "deep contextualized word representations"
    - "cross topic argument mining from heterogeneous sources using attention based neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural end to end learning for computational argumentation mining"
    - "what is the essence of a claim cross domain claim identification"
    - "attention is all you need"
    - "understanding and detecting supporting arguments of diverse types"

Connected through hyperedge HE-5 (induced by source paper "argument mining for understanding peer reviews"):
  with members:
    - "deep contextualized word representations"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "argument mining with structured svms and rnns"
    - "understanding and detecting supporting arguments of diverse types"
    - "parsing argumentation structures in persuasive essays"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "the evolution of argumentation mining from models to social media and emerging tools"):
  with members:
    - "claudette an automated detector of potentially unfair clauses in online terms of service"
    - "argumentation mining exploiting multiple sources and background knowledge"
    - "multi task learning for argumentation mining in low resource settings"
    - "cross topic argument mining from heterogeneous sources using attention based neural networks"
    - "towards automated factchecking developing an annotation schema and benchmark for consistent automated claim detection"
    - "the argument reasoning comprehension task identification and reconstruction of implicit warrants"
    - "mitre at semeval 2016 task 6 transfer learning for stance detection"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-7 (induced by source paper "imho fine tuning improves claim detection"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "glomo unsupervisedly learned relational graphs as transferable representations"
    - "neural end to end learning for computational argumentation mining"
    - "what is the essence of a claim cross domain claim identification"
    - "parsing argumentation structures in persuasive essays"

Connected through hyperedge HE-8 (induced by source paper "seeing things from a different angle discovering diverse perspectives about claims"):
  with members:
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "twowingos a two wing optimization strategy for evidential claim verification"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "understanding and detecting supporting arguments of diverse types"
    - "parsing argumentation structures in persuasive essays"

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)