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: "learning algorithms for keyphrase extraction"
  Abstract: Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a wide variety of tasks for which keyphrases are useful, as we discuss in this paper. We approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. We evaluate the performance of nine different configurations of C4.5. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for automatically extracting keyphrases from text. The experimental results support the claim that a custom-designed algorithm (GenEx), incorporating specialized procedural domain knowledge, can generate better keyphrases than a generalpurpose algorithm (C4.5). Subjective human evaluation of the keyphrases generated by Extractor suggests that about 80% of the keyphrases are acceptable to human readers. This level of performance should be satisfactory for a wide variety of applications.

Connected through hyperedge HE-1 (induced by source paper "two huge title and keyword generation corpora of research articles"):
  with members:
    - "neural keyphrase generation via reinforcement learning with adaptive rewards"
    - "sentiment analysis of czech texts an algorithmic survey"
    - "a review of keyphrase extraction"
    - "efficiency metrics for data driven models a text summarization case study"
    - "keyphrase generation a multi aspect survey"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "data driven summarization of scientific articles"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "this email could save your life introducing the task of email subject line generation"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "metric for automatic machine translation evaluation based on universal sentence representations"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "sentence simplification with deep reinforcement learning"
    - "actionable email intent modeling with reparametrized rnns"

Connected through hyperedge HE-3 (induced by source paper "keyphrase generation a multi aspect survey"):
  with members:
    - "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction"
    - "neural keyphrase generation via reinforcement learning with adaptive rewards"
    - "a review of keyphrase extraction"
    - "keyphrase generation a text summarization struggle"
    - "efficiency metrics for data driven models a text summarization case study"
    - "deep reinforcement learning for sequence to sequence models"
    - "keyphrase generation with correlation constraints"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"

Connected through hyperedge HE-4 (induced by source paper "keyphrase extraction from scholarly articles as sequence labeling using contextualized embeddings"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert a pretrained language model for scientific text"
    - "transformer xl attentive language models beyond a fixed length context"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "keyphrase extraction from disaster related tweets"):
  with members:
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "deep contextualized word representations"
    - "dissecting contextual word embeddings architecture and representation"
    - "deep keyphrase generation"
    - "semi supervised discovery of informative tweets during the emerging disasters"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "recurrent neural network for text classification with multi task learning"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

Connected through hyperedge HE-6 (induced by source paper "keyphrase generation a text summarization struggle"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "deep keyphrase generation"
    - "get to the point summarization with pointer generator networks"
    - "what is the role of recurrent neural networks rnns in an image caption generator"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "visual summarization of scholarly videos using word embeddings and keyphrase extraction"):
  with members:
    - "unsupervised keyphrase extraction with multipartite graphs"
    - "enriching word vectors with subword information"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "scake semantic connectivity aware keyword extraction"):
  with members:
    - "unsupervised keyphrase extraction with multipartite graphs"
    - "keyword and keyphrase extraction using centrality measures on collocation networks"
    - "statistical keyword detection in literary corpora"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "kea practical automatic keyphrase extraction"

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.LG(Machine Learning)