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 to extract keyphrases from text"
  Abstract: Many academic journals ask their authors to provide a list of about five to fifteen key words, 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 surprisingly wide variety of tasks for which keyphrases are useful, as we discuss in this paper. Recent commercial software, such as Microsoft?s Word 97 and Verity?s Search 97, includes algorithms that automatically extract keyphrases from documents. 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. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for this task. The third set of experiments examines the performance of GenEx on the task of metadata generation, relative to the performance of Microsoft?s Word 97. The fourth and final set of experiments investigates the performance of GenEx on the task of highlighting, relative to Verity?s Search 97. The experimental results support the claim that a specialized learning algorithm (GenEx) can generate better keyphrases than a general-purpose learning algorithm (C4.5) and the non-learning algorithms that are used in commercial software (Word 97 and Search 97).

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 "complex network based supervised keyword extractor"):
  with members:
    - "scake semantic connectivity aware keyword extraction"
    - "unsupervised keyphrase extraction with multipartite graphs"
    - "xgboost a scalable tree boosting system"
    - "supervised topical key phrase extraction of news stories using crowdsourcing light filtering and co reference normalization"
    - "statistical keyword detection in literary corpora"
    - "smote synthetic minority over sampling technique"
    - "kea practical automatic keyphrase extraction"

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 "outline generation understanding the inherent content structure of documents"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "deep keyphrase generation"
    - "automated phrase mining from massive text corpora"
    - "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"

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 "an evaluation of keyword extraction from online communication for the characterisation of social relations"):
  with members:
    - "coherent keyphrase extraction via web mining"

Connected through hyperedge HE-7 (induced by source paper "a new keyphrases extraction method based on suffix tree data structure for arabic documents clustering"):
  with members:
    - "arabic text summarization based on latent semantic analysis to enhance arabic documents clustering"
    - "a comparative study of root based and stem based approaches for measuring the similarity between arabic words for arabic text mining applications"
    - "kea practical automatic keyphrase extraction"
    - "extraction of keyphrases from text evaluation of four algorithms"

Connected through hyperedge HE-8 (induced by source paper "arabic keyphrase extraction using linguistic knowledge and machine learning techniques"):
  with members:
    - "learning algorithms for keyphrase extraction"
    - "mining the web for lexical knowledge to improve keyphrase extraction learning from labeled and unlabeled data"
    - "kea practical automatic keyphrase extraction"
    - "extraction of keyphrases from text evaluation of four algorithms"

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)