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 hierarchical text classification"
  Abstract: Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real-world applications. Recently, deep neural models are gaining increasing popularity for text classification due to their expressive power and minimum requirement for feature engineering. However, applying deep neural networks for hierarchical text classification remains challenging, because they heavily rely on a large amount of training data and meanwhile cannot easily determine appropriate levels of documents in the hierarchical setting. In this paper, we propose a weakly-supervised neural method for hierarchical text classification. Our method does not require a large amount of training data but requires only easy-to-provide weak supervision signals such as a few class-related documents or keywords. Our method effectively leverages such weak supervision signals to generate pseudo documents for model pre-training, and then performs self-training on real unlabeled data to iteratively refine the model. During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism. Experiments on three datasets from different domains demonstrate the efficacy of our method compared with a comprehensive set of baselines.

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 neural text classification"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "deep hyperspherical learning"

Connected through hyperedge HE-4 (induced by source paper "time weighted attentional session aware recommender system"):
  with members:
    - "towards neural mixture recommender for long range dependent user sequences"
    - "multi task learning for email search ranking with auxiliary query clustering"
    - "sequence aware recommender systems"
    - "recurrent neural networks for long and short term sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "mining entity synonyms with efficient neural set generation"
    - "session aware information embedding for e commerce product recommendation"
    - "improving session recommendation with recurrent neural networks by exploiting dwell time"

Connected through hyperedge HE-5 (induced by source paper "hyperbolic interaction model for hierarchical multi label classification"):
  with members:
    - "hyperbolic attention networks"
    - "combined convolutional and recurrent neural networks for hierarchical classification of images"
    - "skip gram word embeddings in hyperbolic space"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "hyperbolic neural networks"
    - "embedding text in hyperbolic spaces"
    - "joint embedding of words and labels for text classification"
    - "representation tradeoffs for hyperbolic embeddings"

Connected through hyperedge HE-6 (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 neural 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-7 (induced by source paper "efficient path prediction for semi supervised and weakly supervised hierarchical text classification"):
  with members:
    - "weakly supervised neural 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 deep recurrent neural networks for basic dance step generation"):
  with members:
    - "audio super resolution using neural networks"
    - "end to end musical key estimation using a convolutional neural network"
    - "madmom a new python audio and music signal processing library"
    - "audio event detection using weakly labeled data"
    - "generative choreography using deep learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

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)