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 skill transfer from supervised language tasks to reading comprehension"
  Abstract: Reading comprehension is a challenging task in natural language processing and requires a set of skills to be solved. While current approaches focus on solving the task as a whole, in this paper, we propose to use a neural network `skill' transfer approach. We transfer knowledge from several lower-level language tasks (skills) including textual entailment, named entity recognition, paraphrase detection and question type classification into the reading comprehension model. #R##N#We conduct an empirical evaluation and show that transferring language skill knowledge leads to significant improvements for the task with much fewer steps compared to the baseline model. We also show that the skill transfer approach is effective even with small amounts of training data. Another finding of this work is that using token-wise deep label supervision for text classification improves the performance of transfer learning.

Connected through hyperedge HE-1 (induced by source paper "supervised contextual embeddings for transfer learning in natural language processing tasks"):
  with members:
    - "polyglot semantic role labeling"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "linguistically informed self attention for semantic role labeling"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a minimal span based neural constituency parser"
    - "learned in translation contextualized word vectors"
    - "semi supervised sequence tagging with bidirectional language models"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-2 (induced by source paper "simple and effective semi supervised question answering"):
  with members:
    - "deep contextualized word representations"
    - "neural models for key phrase detection and question generation"
    - "contextualized word representations for reading comprehension"
    - "two stage synthesis networks for transfer learning in machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "a unified query based generative model for question generation and question answering"
    - "supervised and unsupervised transfer learning for question answering"
    - "learned in translation contextualized word vectors"

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)