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: "global vectors for node representations"
  Abstract: Most network embedding algorithms consist in measuring co-occur-rences of nodes via random walks then learning the embeddings using Skip-Gram with Negative Sampling. While it has proven to be a relevant choice, there are alternatives, such as GloVe, which has not been investigated yet for network embedding. Even though SGNS better handles non co-occurrence than GloVe, it has a worse time-complexity. In this paper, we propose a matrix factorization approach for network embedding, inspired by GloVe, that better handles non co-occurrence with a competitive time-complexity. We also show how to extend this model to deal with networks where nodes are documents, by simultaneously learning word, node and document representations. Quantitative evaluations show that our model achieves state-of-the-art performance, while not being so sensitive to the choice of hyper-parameters. Qualitatively speaking, we show how our model helps exploring a network of documents by generating complementary network-oriented and content-oriented keywords.

Connected through hyperedge HE-1 (induced by source paper "inductive document network embedding with topic word attention"):
  with members:
    - "link prediction with mutual attention for text attributed networks"
    - "verse versatile graph embeddings from similarity measures"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "node2vec scalable feature learning for networks"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "document network projection in pretrained word embedding space"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "monet debiasing graph embeddings via the metadata orthogonal training unit"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "compositional fairness constraints for graph embeddings"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "a tutorial on network embeddings"
    - "enhancing network embedding with auxiliary information an explicit matrix factorization perspective"
    - "a survey on network embedding"
    - "the ground truth about metadata and community detection in networks"

Connected through hyperedge HE-4 (induced by source paper "document network embedding coping for missing content and missing links"):
  with members:
    - "neural ir meets graph embedding a ranking model for product search"
    - "representation learning for recommender systems with application to the scientific literature"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-5 (induced by source paper "representation learning for recommender systems with application to the scientific literature"):
  with members:
    - "verse versatile graph embeddings from similarity measures"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "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)