human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "models for predicting community specific interest in news articles"):
  Abstract: In this work, we ask two questions: 1. Can we predict the type of community interested in a news article using only features from the article content? and 2. How well do these models generalize over time? To answer these questions, we compute well-studied content-based features on over 60K news articles from 4 communities on reddit.com. We train and test models over three different time periods between 2015 and 2017 to demonstrate which features degrade in performance the most due to concept drift. Our models can classify news articles into communities with high accuracy, ranging from 0.81 ROC AUC to 1.0 ROC AUC. However, while we can predict the community-specific popularity of news articles with high accuracy, practitioners should approach these models carefully. Predictions are both community-pair dependent and feature group dependent. Moreover, these feature groups generalize over time differently, with some only degrading slightly over time, but others degrading greatly. Therefore, we recommend that community-interest predictions are done in a hierarchical structure, where multiple binary classifiers can be used to separate community pairs, rather than a traditional multi-class model. Second, these models should be retrained over time based on accuracy goals and the availability of training data.

Includes members:
  Member "sampling the news producers a large news and feature data set for the study of the complex media landscape":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nela gt 2019 a large multi labelled news dataset for the study of misinformation in news articles")
  Member "identifying the social signals that drive online discussions a case study of reddit communities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "detecting toxicity in news articles application to bulgarian")
  Member "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the pushshift reddit dataset")
  Member "the impact of crowds on news engagement a reddit case study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the pushshift reddit dataset")
  Member "characterizing the language of online communities and its relation to community reception":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vocabulary based method for quantifying controversy in social media")
  Member "breaking the news first impressions matter on online news":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sentiment dynamics in social media news channels")
  Member "leveraging joint interactions for credibility analysis in news communities":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "checkthat at clef 2020 enabling the automatic identification and verification of claims in social media")
  Member "talking to the crowd what do people react to in online discussions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the anatomy of reddit an overview of academic research")

Question: Please tell me which class the source paper 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)