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: "combating fake news a survey on identification and mitigation techniques"
  Abstract: The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users' engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern-day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.

Connected through hyperedge HE-1 (induced by source paper "interpretable rumor detection in microblogs by attending to user interactions"):
  with members:
    - "the fact extraction and verification fever shared task"
    - "self attention with relative position representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "attention is all you need"
    - "computational fact checking from knowledge networks"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-2 (induced by source paper "information credibility in the social web contexts approaches and open issues"):
  with members:
    - "on the inevitability of online echo chambers"
    - "automated fact checking task formulations methods and future directions"
    - "the spread of low credibility content by social bots"
    - "automated hate speech detection and the problem of offensive language"
    - "fake news detection on social media a data mining perspective"
    - "echo chambers in the age of misinformation"

Connected through hyperedge HE-3 (induced by source paper "modeling conversation structure and temporal dynamics for jointly predicting rumor stance and veracity"):
  with members:
    - "a semi supervised approach to message stance classification"
    - "stance classification for rumour analysis in twitter exploiting affective information and conversation structure"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "all in one multi task learning for rumour verification"
    - "identifying and understanding user reactions to deceptive and trusted social news sources"
    - "cross lingual cross platform rumor verification pivoting on multimedia content"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"

Connected through hyperedge HE-4 (induced by source paper "open issues in combating fake news interpretability as an opportunity"):
  with members:
    - "a survey of evaluation methods and measures for interpretable machine learning"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "mitigating confirmation bias on twitter by recommending opposing views"
    - "beyond news contents the role of social context for fake news detection"
    - "some like it hoax automated fake news detection in social networks"

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)