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: "finding convincing arguments using scalable bayesian preference learning"
  Abstract: We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard rat- ings or rankings. In contrast to previous work, we avoid the need for separate methods to perform quality control on training data, predict rankings and perform pairwise classification. Bayesian approaches are an effective solution when faced with sparse or noisy training data, but have not previously been used to identify convincing arguments. One issue is scalability, which we address by developing a stochastic variational inference method for Gaussian process (GP) preference learning. We show how our method can be applied to predict argument convincingness from crowdsourced data, outperforming the previous state-of-the-art, particularly when trained with small amounts of unreliable data. We demonstrate how the Bayesian approach enables more effective active learning, thereby reducing the amount of data required to identify convincing arguments for new users and domains. While word embeddings are principally used with neural networks, our results show that word embeddings in combination with linguistic features also benefit GPs when predicting argument convincingness.

Connected through hyperedge HE-1 (induced by source paper "preference based interactive multi document summarisation"):
  with members:
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "viana visual interactive annotation of argumentation"
    - "better rewards yield better summaries learning to summarise without references"
    - "improving abstraction in text summarization"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "counterfactual learning from human proofreading feedback for semantic parsing"
    - "can neural machine translation be improved with user feedback"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "interactive text ranking with bayesian optimisation a case study on community qa and summarisation"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "passage ranking with weak supervision"
    - "preference based interactive multi document summarisation"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"
    - "active learning for interactive neural machine translation of data streams"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "commonsense for generative multi hop question answering tasks"

Connected through hyperedge HE-3 (induced by source paper "scalable bayesian preference learning for crowds"):
  with members:
    - "bayesian heatmaps probabilistic classification with multiple unreliable information sources"
    - "hybrid mst a hybrid active sampling strategy for pairwise preference aggregation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation"
    - "distributed bayesian probabilistic matrix factorization"
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "distributed bayesian probabilistic matrix factorization"

Connected through hyperedge HE-4 (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"
    - "deep contextualized word representations"
    - "parsing argumentation structures in persuasive essays"
    - "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-5 (induced by source paper "a large scale dataset for argument quality ranking construction and analysis"):
  with members:
    - "classification and clustering of arguments with contextualized word embeddings"
    - "automatic argument quality assessment new datasets and methods"
    - "corpus wide argument mining a working solution"
    - "are you convinced choosing the more convincing evidence with a siamese network"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the argument reasoning comprehension task identification and reconstruction of implicit warrants"

Connected through hyperedge HE-6 (induced by source paper "automatic argument quality assessment new datasets and methods"):
  with members:
    - "classification and clustering of arguments with contextualized word embeddings"
    - "are you convinced choosing the more convincing evidence with a siamese network"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-7 (induced by source paper "does my rebuttal matter insights from a major nlp conference"):
  with members:
    - "argument mining for understanding peer reviews"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "opinion dynamics and wisdom under out group discrimination"
    - "a computational approach to politeness with application to social factors"

Connected through hyperedge HE-8 (induced by source paper "april interactively learning to summarise by combining active preference learning and reinforcement learning"):
  with members:
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "a deep reinforced model for abstractive summarization"
    - "stochastic structured prediction under bandit feedback"
    - "sequence level training with recurrent neural networks"
    - "active ranking using pairwise comparisons"

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)