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: "fake cures user centric modeling of health misinformation in social media"
  Abstract: Social media's unfettered access has made it an important venue for health discussion and a resource for patients and their loved ones. However, the quality of the information available, as well as the motivations of its posters, has been questioned. This work examines the individuals on social media that are posting questionable health-related information, and in particular promoting cancer treatments which have been shown to be ineffective (making it a kind of misinformation, willful or not). Using a multi-stage user selection process, we study 4,212 Twitter users who have posted about one of 139 such "treatments", and compare them to a baseline of users generally interested in cancer. Considering features capturing user attributes, writing style, and sentiment, we build a classifier which is able to identify users prone to propagate such misinformation at an accuracy of over 90%, providing a potential tool for public health officials to identify such individuals for preventive intervention.

Connected through hyperedge HE-1 (induced by source paper "discovering differential features adversarial learning for information credibility evaluation"):
  with members:
    - "the role of user profile for fake news detection"
    - "different absorption from the same sharing sifted multi task learning for fake news detection"
    - "multinomial adversarial networks for multi domain text classification"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "deep neural networks for bot detection"
    - "multi task learning by deep collaboration and application in facial landmark detection"
    - "neural networks for information retrieval"
    - "like sheep among wolves characterizing hateful users on twitter"

Connected through hyperedge HE-2 (induced by source paper "towards automatic detection of misinformation in online medical videos"):
  with members:
    - "youtube for patient education a deep learning approach for understanding medical knowledge from user generated videos"
    - "a deep learning approach for multimodal deception detection"
    - "automatic detection of fake news"
    - "fake news detection on social media a data mining perspective"

Connected through hyperedge HE-3 (induced by source paper "differences between health related news articles from reliable and unreliable media"):
  with members:
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "catching zika fever application of crowdsourcing and machine learning for tracking health misinformation on twitter"
    - "fake news detection on social media a data mining perspective"

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.CY(Computers and Society)