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: "the rise of guardians fact checking url recommendation to combat fake news"
  Abstract: A large body of research work and efforts have been focused on detecting fake news and building online fact-check systems in order to debunk fake news as soon as possible. Despite the existence of these systems, fake news is still wildly shared by online users. It indicates that these systems may not be fully utilized. After detecting fake news, what is the next step to stop people from sharing it? How can we improve the utilization of these fact-check systems? To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities. We found that the guardians usually took less than one day to reply to claims in online conversations and took another day to spread verified information to hundreds of millions of followers. Our proposed recommendation model outperformed four state-of-the-art models by 11%~33%. Our source code and dataset are available at this http URL.

Connected through hyperedge HE-1 (induced by source paper "attributed multi relational attention network for fact checking url recommendation"):
  with members:
    - "learning from fact checkers analysis and generation of fact checking language"
    - "neural graph collaborative filtering"
    - "harmonious attention network for person re identification"
    - "collaborative memory network for recommendation systems"
    - "large scale learnable graph convolutional networks"
    - "semi supervised content based detection of misinformation via tensor embeddings"
    - "nais neural attentive item similarity model for recommendation"
    - "fake news detection in social networks via crowd signals"

Connected through hyperedge HE-2 (induced by source paper "the future of misinformation detection new perspectives and trends"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "fighting fake news image splice detection via learned self consistency"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "explainable recommendation a survey and new perspectives"
    - "detection and resolution of rumours in social media a survey"

Connected through hyperedge HE-3 (induced by source paper "false news on social media a data driven survey"):
  with members:
    - "the junk news aggregator examining junk news posted on facebook starting with the 2018 us midterm elections"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "automated fact checking task formulations methods and future directions"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "anatomy of an online misinformation network"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "detection and resolution of rumours in social media a survey"
    - "the spread of low credibility content by social bots"

Connected through hyperedge HE-4 (induced by source paper "learning from fact checkers analysis and generation of fact checking language"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fever a large scale dataset for fact extraction and verification"
    - "variational memory encoder decoder"
    - "fact checking in community forums"
    - "automated hate speech detection and the problem of offensive language"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"

Connected through hyperedge HE-5 (induced by source paper "towards reliable online clickbait video detection a content agnostic approach"):
  with members:
    - "the age of social sensing"
    - "fighting fake news image splice detection via learned self consistency"
    - "in ictu oculi exposing ai generated fake face videos by detecting eye blinking"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "clickbait identification using neural networks"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "we used neural networks to detect clickbaits you won t believe what happened next"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-6 (induced by source paper "homogeneity based transmissive process to model true and false news in social networks"):
  with members:
    - "false information on web and social media a survey"
    - "limiting the spread of fake news on social media platforms by evaluating users trustworthiness"
    - "fake news detection in social networks via crowd signals"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "fake news mitigation via point process based intervention"
    - "joint modeling of topics citations and topical authority in academic corpora"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "hierarchical dirichlet scaling process"

Connected through hyperedge HE-7 (induced by source paper "fake news detection using deep learning"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "studying fake news via network analysis detection and mitigation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ti cnn convolutional neural networks for fake news detection"
    - "recent trends in deep learning based natural language processing"
    - "stop clickbait detecting and preventing clickbaits in online news media"

Connected through hyperedge HE-8 (induced by source paper "fake news detection using deep learning"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "fake news detection on social media using geometric deep learning"
    - "studying fake news via network analysis detection and mitigation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ti cnn convolutional neural networks for fake news detection"
    - "attention is all you need"

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)