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: "a generalized recurrent neural architecture for text classification with multi task learning"
  Abstract: Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. However, most previous works only consider simple or weak interactions, thereby failing to model complex correlations among three or more tasks. In this paper, we propose a multi-task learning architecture with four types of recurrent neural layers to fuse information across multiple related tasks. The architecture is structurally flexible and considers various interactions among tasks, which can be regarded as a generalized case of many previous works. Extensive experiments on five benchmark datasets for text classification show that our model can significantly improve performances of related tasks with additional information from others.

Connected through hyperedge HE-1 (induced by source paper "an external knowledge enhanced multi label charge prediction approach with label number learning"):
  with members:
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "self attention with relative position representations"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "empirical study of deep learning for text classification in legal document review"
    - "sgm sequence generation model for multi label classification"
    - "attnconvnet at semeval 2018 task 1 attention based convolutional neural networks for multi label emotion classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "multi task learning for universal sentence representations what syntactic and semantic information is captured"):
  with members:
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "end to end neural coreference resolution"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "semi supervised multitask learning for sequence labeling"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-3 (induced by source paper "learning robust transferable sentence representations for text classification"):
  with members:
    - "few shot text classification with pre trained word embeddings and a human in the loop"
    - "universal sentence encoder"
    - "an efficient framework for learning sentence representations"
    - "learning to generate reviews and discovering sentiment"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-4 (induced by source paper "multi task deep learning for legal document translation summarization and multi label classification"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "attention is all you need"
    - "generative adversarial network for abstractive text summarization"
    - "exploring the use of text classification in the legal domain"
    - "an overview of multi task learning in deep neural networks"
    - "one model to learn them all"
    - "recurrent neural network for text classification with multi task learning"

Connected through hyperedge HE-5 (induced by source paper "multi task label embedding for text classification"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "generating sequences with recurrent neural networks"
    - "representation learning a review and new perspectives"

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)