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: "mixing dirichlet topic models and word embeddings to make lda2vec"
  Abstract: Distributed dense word vectors have been shown to be effective at capturing token-level semantic and syntactic regularities in language, while topic models can form interpretable representations over documents. In this work, we describe lda2vec, a model that learns dense word vectors jointly with Dirichlet-distributed latent document-level mixtures of topic vectors. In contrast to continuous dense document representations, this formulation produces sparse, interpretable document mixtures through a non-negative simplex constraint. Our method is simple to incorporate into existing automatic differentiation frameworks and allows for unsupervised document representations geared for use by scientists while simultaneously learning word vectors and the linear relationships between them.

Connected through hyperedge HE-1 (induced by source paper "improving document classification with multi sense embeddings"):
  with members:
    - "topic modeling in embedding spaces"
    - "the dynamic embedded topic model"
    - "improving topic models with latent feature word representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "probabilistic fasttext for multi sense word embeddings"
    - "efficient vector representation for documents through corruption"

Connected through hyperedge HE-2 (induced by source paper "topic modeling in embedding spaces"):
  with members:
    - "improving topic models with latent feature word representations"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "metalda a topic model that efficiently incorporates meta information"
    - "jointly learning word embeddings and latent topics"
    - "nonparametric spherical topic modeling with word embeddings"
    - "neural variational inference for text processing"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "column2vec structural understanding via distributed representations of database schemas"):
  with members:
    - "data curation with deep learning vision towards self driving data curation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeper deep entity resolution"
    - "vector embedding of wikipedia concepts and entities"
    - "graph2vec learning distributed representations of graphs"
    - "generative topic embedding a continuous representation of documents extended version with proofs"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "semantic concept spaces guided topic model refinement using word embedding projections"):
  with members:
    - "explainer a visual analytics framework for interactive and explainable machine learning"
    - "improving topic models with latent feature word representations"
    - "a visual interaction framework for dimensionality reduction based data exploration"
    - "recent research advances on interactive machine learning"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "machine teaching a new paradigm for building machine learning systems"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "mixing syntagmatic and paradigmatic information for concept detection"):
  with members:
    - "improving topic models with latent feature word representations"
    - "detecting large concept extensions for conceptual analysis"
    - "interpretable probabilistic embeddings bridging the gap between topic models and neural networks"
    - "nonparametric spherical topic modeling with word embeddings"
    - "probabilistic latent semantic analysis"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "hybrid variational gibbs collapsed inference in topic models"

Connected through hyperedge HE-6 (induced by source paper "discovering topics with neural topic models built from plsa assumptions"):
  with members:
    - "texttovec deep contextualized neural autoregressive topic models of language with distributed compositional prior"
    - "kaggle lshtc4 winning solution"
    - "continuous time dynamic topic models"
    - "multilingual topic models for unaligned text"
    - "natural language processing almost from scratch"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-7 (induced by source paper "two computational models for analyzing political attention in social media"):
  with members:
    - "the digital architectures of social media comparing political campaigning on facebook twitter instagram and snapchat in the 2016 u s election"
    - "exploring the political agenda of the european parliament using a dynamic topic modeling approach"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "imparting interpretability to word embeddings while preserving semantic structure"):
  with members:
    - "analytical methods for interpretable ultradense word embeddings"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "semantic structure and interpretability of word embeddings"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "rotations and interpretability of word embeddings the case of the russian language"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "spine sparse interpretable neural embeddings"
    - "jointly learning word embeddings and latent topics"

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)