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: "parsing argumentation structures in persuasive essays"
  Abstract: In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globally optimizes argument component types and argumentative relations using integer linear programming. We show that our model considerably improves the performance of base classifiers and significantly outperforms challenging heuristic baselines. Moreover, we introduce a novel corpus of persuasive essays annotated with argumentation structures. We show that our annotation scheme and annotation guidelines successfully guide human annotators to substantial agreement. This corpus and the annotation guidelines are freely available for ensuring reproducibility and to encourage future research in computational argumentation.

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"
    - "argumentation mining in user generated web discourse"
    - "neural end to end learning for computational argumentation mining"
    - "easy over hard a case study on deep learning"
    - "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"
    - "argumentation mining in user generated web discourse"
    - "attention is all you need"
    - "advances in pre training distributed word representations"
    - "argument mining with structured svms and rnns"
    - "autoencoder feature selector"
    - "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 "is it time to swish comparing deep learning activation functions across nlp tasks"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "self normalizing neural networks"
    - "neural end to end learning for computational argumentation mining"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "searching for activation functions"
    - "cross lingual and cross domain discourse segmentation of entire documents"
    - "revise saturated activation functions"

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"
    - "argumentation mining in user generated web discourse"
    - "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"

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"
    - "argumentation mining in user generated web discourse"
    - "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"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "are you convinced choosing the more convincing evidence with a siamese network"):
  with members:
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "exploring the role of prior beliefs for argument persuasion"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "deep contextualized word representations"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "recurrent batch normalization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "argumentative relation classification as plausibility ranking"):
  with members:
    - "dissecting content and context in argumentative relation analysis"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self normalizing neural networks"
    - "a mention ranking model for abstract anaphora resolution"
    - "attention is all you need"
    - "adam a method for stochastic optimization"
    - "a pdtb styled end to end discourse parser"

Connected through hyperedge HE-8 (induced by source paper "corpus wide argument mining a working solution"):
  with members:
    - "fast end to end wikification"
    - "are you convinced choosing the more convincing evidence with a siamese network"
    - "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"
    - "an empirical evaluation of various deep learning architectures for bi sequence classification tasks"
    - "taking into account the differences between actively and passively acquired data the case of active learning with support vector machines for imbalanced datasets"
    - "active learning with statistical models"

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)