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 news suggestions for populating wikipedia entity pages"
  Abstract: Wikipedia entity pages are a valuable source of information for direct consumption and for knowledge-base construction, update and maintenance. Facts in these entity pages are typically supported by references. Recent studies show that as much as 20% of the references are from online news sources. However, many entity pages are incomplete even if relevant information is already available in existing news articles. Even for the already present references, there is often a delay between the news article publication time and the reference time. In this work, we therefore look at Wikipedia through the lens of news and propose a novel news-article suggestion task to improve news coverage in Wikipedia, and reduce the lag of newsworthy references. Our work finds direct application, as a precursor, to Wikipedia page generation and knowledge-base acceleration tasks that rely on relevant and high quality input sources.   We propose a two-stage supervised approach for suggesting news articles to entity pages for a given state of Wikipedia. First, we suggest news articles to Wikipedia entities (article-entity placement) relying on a rich set of features which take into account the salience and relative authority of entities, and the novelty of news articles to entity pages. Second, we determine the exact section in the entity page for the input article (article-section placement) guided by class-based section templates. We perform an extensive evaluation of our approach based on ground-truth data that is extracted from external references in Wikipedia. We achieve a high precision value of up to 93% in the article-entity suggestion stage and upto 84% for the article-section placement. Finally, we compare our approach against competitive baselines and show significant improvements.

Connected through hyperedge HE-1 (induced by source paper "citation recommendation approaches and datasets"):
  with members:
    - "content based citation recommendation"
    - "hyperdoc2vec distributed representations of hypertext documents"
    - "core elements in the process of citing publications conceptual overview of the literature"
    - "cross language citation recommendation via hierarchical representation learning on heterogeneous graph"
    - "machine learning vs rules and out of the box vs retrained an evaluation of open source bibliographic reference and citation parsers"
    - "on measuring bias in online information"
    - "all fingers are not equal intensity of references in scientific articles"
    - "finding news citations for wikipedia"

Connected through hyperedge HE-2 (induced by source paper "citation needed a taxonomy and algorithmic assessment of wikipedia s verifiability"):
  with members:
    - "fever a large scale dataset for fact extraction and verification"
    - "towards automated factchecking developing an annotation schema and benchmark for consistent automated claim detection"
    - "fine grained citation span for references in wikipedia"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "finding news citations for wikipedia"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "swat a system for detecting salient wikipedia entities in texts"):
  with members:
    - "wiser a semantic approach for expert finding in academia based on entity linking"
    - "allennlp a deep semantic natural language processing platform"
    - "reading wikipedia to answer open domain questions"
    - "xgboost a scalable tree boosting system"
    - "probabilistic bag of hyperlinks model for entity linking"
    - "deepwalk online learning of social representations"
    - "axioms for centrality"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "approaches for enriching and improving textual knowledge bases"):
  with members:
    - "fine grained citation span for references in wikipedia"
    - "finding news citations for wikipedia"
    - "how much is wikipedia lagging behind news"

Connected through hyperedge HE-5 (induced by source paper "wikiref wikilinks as a route to recommending appropriate references for scientific wikipedia pages"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-6 (induced by source paper "expedition a time aware exploratory search system designed for scholars"):
  with members:
    - "history by diversity helping historians search news archives"

Connected through hyperedge HE-7 (induced by source paper "finding news citations for wikipedia"):
  with members:
    - "how much is wikipedia lagging behind news"

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.IR(Information Retrieval)