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: "bayesian ensembles of crowds and deep learners for sequence tagging"
  Abstract: Current methods for sequence tagging, a core task in NLP, are data hungry. Crowdsourcing is a relatively cheap way to obtain labeled data, but the annotators are unreliable, so redundant labeling and aggregation techniques are required. We evaluate multiple models of annotator reliability and develop a Bayesian method for aggregating sequence labels from multiple annotators. Typically, the process of data collection, aggregation and training a sequence tagger is a pipeline of discrete steps. We integrate these steps by training black-box sequence taggers as components in the aggregation model and accounting for their unreliability. We evaluate our model on named entity recognition and information extraction tasks, showing that our method outperforms previous methods, particularly in small data scenarios that are encountered at the beginning of a crowdsourcing process. Our code is published to encourage adaptation and reuse.

Connected through hyperedge HE-1 (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"
    - "a bayesian approach for sequence tagging with crowds"
    - "end to end incremental learning"
    - "progressive neural networks"
    - "neural end to end learning for computational argumentation mining"

Connected through hyperedge HE-2 (induced by source paper "robust argument unit recognition and classification"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural end to end learning for computational argumentation mining"
    - "understanding and detecting supporting arguments of diverse types"

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)