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: "improving topic models with latent feature word representations"
  Abstract: Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document clustering and document classification tasks, especially on datasets with few or short documents.

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"
    - "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"
    - "weakly supervised hierarchical text classification"

Connected through hyperedge HE-2 (induced by source paper "dynamic transformation of prior knowledge intobayesian models for data streams"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bayesian models of data streams with hierarchical power priors"
    - "session based recommendations with recurrent neural networks"
    - "dynamic poisson factorization"
    - "distributed representations of words and phrases and their compositionality"
    - "continuous time dynamic topic models"

Connected through hyperedge HE-3 (induced by source paper "conversational structure aware and context sensitive topic model for online discussions"):
  with members:
    - "context aware image annotation in active learning"
    - "eliminating search intent bias in learning to rank"
    - "knowledge guided text structuring in clinical trials"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "jointly learning word embeddings and latent topics"
    - "event detection tracking and visualization in twitter a mention anomaly based approach"
    - "probabilistic latent semantic analysis"

Connected through hyperedge HE-4 (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"
    - "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"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "improving document classification with multi sense embeddings"):
  with members:
    - "topic modeling in embedding spaces"
    - "the dynamic embedded topic model"
    - "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"
    - "ltsg latent topical skip gram for mutually learning topic model and vector representations"

Connected through hyperedge HE-6 (induced by source paper "etnlp a visual aided systematic approach to select pre trained embeddings for a downstream task"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "introducing two vietnamese datasets for evaluating semantic models of dis similarity and relatedness"
    - "vncorenlp a vietnamese natural language processing toolkit"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attentive neural network for named entity recognition in vietnamese"
    - "a fast and accurate vietnamese word segmenter"
    - "from word segmentation to pos tagging for vietnamese"

Connected through hyperedge HE-7 (induced by source paper "what you say and how you say it joint modeling of topics and discourse in microblog conversations"):
  with members:
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "topic memory networks for short text classification"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "joint modeling of content and discourse relations in dialogues"
    - "find the conversation killers a predictive study of thread ending posts"
    - "discovering discrete latent topics with neural variational inference"
    - "jointly learning word embeddings and latent topics"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-8 (induced by source paper "deep representation learning for clustering of health tweets"):
  with members:
    - "characterizing transgender health issues in twitter"
    - "clustering and unsupervised anomaly detection with l2 normalized deep auto encoder representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character based neural embeddings for tweet clustering"
    - "multi task learning for mental health using social media text"
    - "tweet2vec character based distributed representations for social media"
    - "enriching word vectors with subword information"

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)