human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi task learning with contextualized word representations for extented named entity recognition"):
  Abstract: Fine-Grained Named Entity Recognition (FG-NER) is critical for many NLP applications. While classical named entity recognition (NER) has attracted a substantial amount of research, FG-NER is still an open research domain. The current state-of-the-art (SOTA) model for FG-NER relies heavily on manual efforts for building a dictionary and designing hand-crafted features. The end-to-end framework which achieved the SOTA result for NER did not get the competitive result compared to SOTA model for FG-NER. In this paper, we investigate how effective multi-task learning approaches are in an end-to-end framework for FG-NER in different aspects. Our experiments show that using multi-task learning approaches with contextualized word representation can help an end-to-end neural network model achieve SOTA results without using any additional manual effort for creating data and designing features.

Includes members:
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "semi supervised sequence modeling with cross view training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "snippext semi supervised opinion mining with augmented data")
  Member "multi task learning for sequence tagging an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form")
  Member "neural adaptation layers for cross domain named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "zero resource cross domain named entity recognition")
  Member "end to end recurrent neural network models for vietnamese named entity recognition word level vs character level":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on recent advances in named entity recognition from deep learning models")
  Member "transfer learning for sequence tagging with hierarchical recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "zero resource cross domain named entity recognition")
  Member "semi supervised multitask learning for sequence labeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploring self supervised regularization for supervised and semi supervised learning")

Question: Please tell me which class the source paper 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)