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: "finding news citations for wikipedia"
  Abstract: An important editing policy in Wikipedia is to provide citations for added statements in Wikipedia pages, where statements can be arbitrary pieces of text, ranging from a sentence to a paragraph. In many cases citations are either outdated or missing altogether.   In this work we address the problem of finding and updating news citations for statements in entity pages. We propose a two-stage supervised approach for this problem. In the first step, we construct a classifier to find out whether statements need a news citation or other kinds of citations (web, book, journal, etc.). In the second step, we develop a news citation algorithm for Wikipedia statements, which recommends appropriate citations from a given news collection. Apart from IR techniques that use the statement to query the news collection, we also formalize three properties of an appropriate citation, namely: (i) the citation should entail the Wikipedia statement, (ii) the statement should be central to the citation, and (iii) the citation should be from an authoritative source.   We perform an extensive evaluation of both steps, using 20 million articles from a real-world news collection. Our results are quite promising, and show that we can perform this task with high precision and at scale.

Connected through hyperedge HE-1 (induced by source paper "quantifying engagement with citations on wikipedia"):
  with members:
    - "citation needed a taxonomy and algorithmic assessment of wikipedia s verifiability"
    - "mapping the backbone of the humanities through the eyes of wikipedia"
    - "detecting and gauging impact on wikipedia page views"
    - "eliciting new wikipedia users interests via automatically mined questionnaires for a warm welcome not a cold start"
    - "why the world reads wikipedia beyond english speakers"
    - "structuring wikipedia articles with section recommendations"
    - "amplifying the impact of open access wikipedia and the diffusion of science"
    - "why we read wikipedia"

Connected through hyperedge HE-2 (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"
    - "automated news suggestions for populating wikipedia entity pages"

Connected through hyperedge HE-3 (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"
    - "automated news suggestions for populating wikipedia entity pages"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "neural based statement classification for biased language"):
  with members:
    - "selection bias in news coverage learning it fighting it"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "how much is wikipedia lagging behind news"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "approaches for enriching and improving textual knowledge bases"):
  with members:
    - "fine grained citation span for references in wikipedia"
    - "how much is wikipedia lagging behind news"
    - "automated news suggestions for populating wikipedia entity pages"

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)