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: "neural end to end learning for computational argumentation mining"
  Abstract: We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning 'natural' subtasks, in a multi-task learning setup, improves performance.

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"
    - "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 "multi task learning for argumentation mining"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "lstm a search space odyssey"
    - "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-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"
    - "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"
    - "parsing argumentation structures in persuasive essays"
    - "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"
    - "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 "analysis of automatic annotation suggestions for hard discourse level tasks in expert domains"):
  with members:
    - "code switched language models using dual rnns and same source pretraining"
    - "challenges in the automatic analysis of students diagnostic reasoning"
    - "the dalphi annotation framework how its pre annotations can improve annotator efficiency"
    - "learning word vectors for 157 languages"
    - "bayesian ensembles of crowds and deep learners for sequence tagging"
    - "a bayesian approach for sequence tagging with crowds"
    - "end to end incremental learning"
    - "progressive neural networks"

Connected through hyperedge HE-6 (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"
    - "parsing argumentation structures in persuasive essays"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "classification and clustering of arguments with contextualized word embeddings"):
  with members:
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "measuring the similarity of sentential arguments in dialogue"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "is it worth the attention a comparative evaluation of attention layers for argument unit segmentation"):
  with members:
    - "end to end argument mining for discussion threads based on parallel constrained pointer architecture"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "parsing argumentation structures in persuasive essays"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "show attend and tell neural image caption generation with visual attention"
    - "neural machine translation by jointly learning to align and translate"

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)