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: "automated phrase mining from massive text corpora"
  Abstract: As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus. Phrase mining is important in various tasks such as information extraction/retrieval, taxonomy construction, and topic modeling. Most existing methods rely on complex, trained linguistic analyzers, and thus likely have unsatisfactory performance on text corpora of new domains and genres without extra but expensive adaption. Recently, a few data-driven methods have been developed successfully for extraction of phrases from massive domain-specific text. However, none of the state-of-the-art models is fully automated because they require human experts for designing rules or labeling phrases. #R##N#Since one can easily obtain many quality phrases from public knowledge bases to a scale that is much larger than that produced by human experts, in this paper, we propose a novel framework for automated phrase mining, AutoPhrase, which leverages this large amount of high-quality phrases in an effective way and achieves better performance compared to limited human labeled phrases. In addition, we develop a POS-guided phrasal segmentation model, which incorporates the shallow syntactic information in part-of-speech (POS) tags to further enhance the performance, when a POS tagger is available. Note that, AutoPhrase can support any language as long as a general knowledge base (e.g., Wikipedia) in that language is available, while benefiting from, but not requiring, a POS tagger. Compared to the state-of-the-art methods, the new method has shown significant improvements in effectiveness on five real-world datasets across different domains and languages.

Connected through hyperedge HE-1 (induced by source paper "cube2net efficient query specific network construction with data cube organization"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "neural embedding propagation on heterogeneous networks"
    - "relation learning on social networks with multi modal graph edge variational autoencoders"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "place deduplication with embeddings"
    - "query specific knowledge summarization with entity evolutionary networks"
    - "cubenet multi facet hierarchical heterogeneous network construction analysis and mining"
    - "user guided clustering in heterogeneous information networks via motif based comprehensive transcription"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "raw to end name entity recognition in social media"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation"
    - "learned in translation contextualized word vectors"
    - "semi supervised sequence tagging with bidirectional language models"

Connected through hyperedge HE-5 (induced by source paper "outline generation understanding the inherent content structure of documents"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "deep keyphrase generation"
    - "get to the point summarization with pointer generator networks"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "sequence to sequence rnns for text summarization"
    - "a neural attention model for abstractive sentence summarization"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-6 (induced by source paper "hamner headword amplified multi span distantly supervised method for domain specific named entity recognition"):
  with members:
    - "multi grained named entity recognition"
    - "a survey on deep learning for named entity recognition"
    - "a span selection model for semantic role labeling"
    - "deep contextualized word representations"
    - "unsupervised aspect term extraction with b lstm crf using automatically labelled datasets"
    - "a structured self attentive sentence embedding"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-7 (induced by source paper "predicting drug responses by propagating interactions through text enhanced drug gene networks"):
  with members:
    - "metapad meta pattern discovery from massive text corpora"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "image captioning with semantic attention"
    - "self critical sequence training for image captioning"
    - "a critical review of recurrent neural networks for sequence learning"
    - "language models for image captioning the quirks and what works"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "generating an overview report over many documents"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "extract with order for coherent multi document summarization"
    - "dtatg an automatic title generator based on dependency trees"
    - "semi supervised classification with graph convolutional networks"
    - "attsum joint learning of focusing and summarization with neural attention"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"
    - "neural machine translation by jointly learning to align and translate"

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)