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: "scalable topical phrase mining from text corpora"
  Abstract: While most topic modeling algorithms model text corpora with unigrams, human interpretation often relies on inherent grouping of terms into phrases. As such, we consider the problem of discovering topical phrases of mixed lengths. Existing work either performs post processing to the inference results of unigram-based topic models, or utilizes complex n-gram-discovery topic models. These methods generally produce low-quality topical phrases or suffer from poor scalability on even moderately-sized datasets. We propose a different approach that is both computationally efficient and effective. Our solution combines a novel phrase mining framework to segment a document into single and multi-word phrases, and a new topic model that operates on the induced document partition. Our approach discovers high quality topical phrases with negligible extra cost to the bag-of-words topic model in a variety of datasets including research publication titles, abstracts, reviews, and news articles.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a review of keyphrase extraction"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "keyphrase generation with correlation constraints"
    - "learning feature representations for keyphrase extraction"
    - "semi supervised learning for neural keyphrase generation"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-3 (induced by source paper "contrastive reasons detection and clustering from online polarized debate"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "summarizing dialogic arguments from social media"
    - "stance and sentiment in tweets"
    - "using summarization to discover argument facets in online idealogical dialog"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-4 (induced by source paper "comparative analysis of content based personalized microblog recommendations experiments and analysis"):
  with members:
    - "probabilistic latent semantic analysis"
    - "the author topic model for authors and documents"
    - "content based book recommending using learning for text categorization"

Connected through hyperedge HE-5 (induced by source paper "are abstracts enough for hypothesis generation"):
  with members:
    - "validation and topic driven ranking for biomedical hypothesis generation systems"
    - "large scale validation of hypothesis generation systems via candidate ranking"
    - "automated phrase mining from massive text corpora"
    - "moliere automatic biomedical hypothesis generation system"
    - "fasttext zip compressing text classification models"
    - "bag of tricks for efficient text classification"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "validation and topic driven ranking for biomedical hypothesis generation systems"):
  with members:
    - "are abstracts enough for hypothesis generation"
    - "efficient correlated topic modeling with topic embedding"
    - "moliere automatic biomedical hypothesis generation system"
    - "fasttext zip compressing text classification models"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "large scale validation of hypothesis generation systems via candidate ranking"):
  with members:
    - "are abstracts enough for hypothesis generation"
    - "moliere automatic biomedical hypothesis generation system"
    - "fasttext zip compressing text classification models"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "my approach your apparatus"):
  with members:
    - "automated phrase mining from massive text corpora"
    - "fast flexible models for discovering topic correlation across weakly related collections"

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)