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: "weakly supervised neural text classification"
  Abstract: Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly.

Connected through hyperedge HE-1 (induced by source paper "discriminative topic mining via category name guided text embedding"):
  with members:
    - "guiding corpus based set expansion by auxiliary sets generation and co expansion"
    - "spherical text embedding"
    - "higitclass keyword driven hierarchical classification of github repositories"
    - "improving topic models with latent feature word representations"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"

Connected through hyperedge HE-2 (induced by source paper "cate category name guidedword embedding"):
  with members:
    - "guiding corpus based set expansion by auxiliary sets generation and co expansion"
    - "spherical text embedding"
    - "topic modeling in embedding spaces"
    - "higitclass keyword driven hierarchical classification of github repositories"
    - "improving topic models with latent feature word representations"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "distilled wasserstein learning for word embedding and topic modeling"

Connected through hyperedge HE-3 (induced by source paper "spherical text embedding"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "embedding text in hyperbolic spaces"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "weakly supervised hierarchical text classification"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "deep hyperspherical learning"

Connected through hyperedge HE-4 (induced by source paper "a variational approach to weakly supervised document level multi aspect sentiment classification"):
  with members:
    - "variational knowledge graph reasoning"
    - "variational reasoning for question answering with knowledge graph"
    - "interactive attention networks for aspect level sentiment classification"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "aspect level sentiment classification with deep memory network"
    - "rationalizing neural predictions"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "higitclass keyword driven hierarchical classification of github repositories"):
  with members:
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "categorizing the content of github readme files"
    - "universal language model fine tuning for text classification"
    - "weakly supervised hierarchical text classification"
    - "heteromed heterogeneous information network for medical diagnosis"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-6 (induced by source paper "hierarchical text classification with reinforced label assignment"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "end to end reinforcement learning for automatic taxonomy induction"
    - "self critical sequence training for image captioning"
    - "lshtc a benchmark for large scale text classification"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "efficient path prediction for semi supervised and weakly supervised hierarchical text classification"):
  with members:
    - "weakly supervised hierarchical text classification"
    - "hierarchical loss for classification"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised hierarchical text classification"):
  with members:
    - "character level convolutional networks for text classification"
    - "unsupervised deep embedding for clustering analysis"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "distributed representations of words and phrases and their compositionality"

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.IR(Information Retrieval)