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: "sgm sequence generation model for multi label classification"
  Abstract: Multi-label classification is an important yet challenging task in natural language processing. It is more complex than single-label classification in that the labels tend to be correlated. Existing methods tend to ignore the correlations between labels. Besides, different parts of the text can contribute differently for predicting different labels, which is not considered by existing models. In this paper, we propose to view the multi-label classification task as a sequence generation problem, and apply a sequence generation model with a novel decoder structure to solve it. Extensive experimental results show that our proposed methods outperform previous work by a substantial margin. Further analysis of experimental results demonstrates that the proposed methods not only capture the correlations between labels, but also select the most informative words automatically when predicting different labels.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

Connected through hyperedge HE-2 (induced by source paper "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "graph convolutional networks for text classification"
    - "attention based capsule networks with dynamic routing for relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "capsule networks against medical imaging data challenges"
    - "dynamic routing between capsules"
    - "adversarial multi task learning for text classification"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-3 (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"
    - "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"
    - "where and when to look deep siamese attention networks for video based person re identification"

Connected through hyperedge HE-4 (induced by source paper "recursive graphical neural networks for text classification"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph convolutional networks for text classification"
    - "relational inductive biases deep learning and graph networks"
    - "a graph to sequence model for amr to text generation"
    - "joint embedding of words and labels for text classification"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "sentence state lstm for text representation"

Connected through hyperedge HE-5 (induced by source paper "scalable and accurate dialogue state tracking via hierarchical sequence generation"):
  with members:
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "dialog state tracking a neural reading comprehension approach"
    - "on the convergence of adam and beyond"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "global locally self attentive dialogue state tracker"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "key fact as pivot a two stage model for low resource table to text generation"):
  with members:
    - "dual latent variable model for low resource natural language generation in dialogue systems"
    - "universal neural machine translation for extremely low resource languages"
    - "end to end content and plan selection for data to text generation"
    - "learning neural templates for text generation"
    - "phrase based neural unsupervised machine translation"
    - "data to text generation with content selection and planning"
    - "attention is all you need"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "a dual reinforcement learning framework for unsupervised text style transfer"):
  with members:
    - "livebot generating live video comments based on visual and textual contexts"
    - "unsupervised text style transfer using language models as discriminators"
    - "style transfer as unsupervised machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "multi reward reinforced summarization with saliency and entailment"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"

Connected through hyperedge HE-8 (induced by source paper "label aware document representation via hybrid attention for extreme multi label text classification"):
  with members:
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "explicit interaction model towards text classification"
    - "deep extreme multi label learning"
    - "a structured self attentive sentence embedding"
    - "node2vec scalable feature learning for networks"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "neural semantic encoders"

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)