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: "warning signs in communicating the machine learning detection results of misinformation with individuals"
  Abstract: With the prevalence of misinformation online, researchers have focused on developing various machine learning algorithms to detect fake news. However, users' perception of machine learning outcomes and related behaviors have been widely ignored. Hence, this paper proposed to bridge this gap by studying how to pass the detection results of machine learning to the users, and aid their decisions in handling misinformation. An online experiment was conducted, to evaluate the effect of the proposed machine learning warning sign against a control condition. We examined participants' detection and sharing of news. The data showed that warning sign's effects on participants' trust toward the fake news were not significant. However, we found that people's uncertainty about the authenticity of the news dropped with the presence of the machine learning warning sign. We also found that social media experience had effects on users' trust toward the fake news, and age and social media experience had effects on users' sharing decision. Therefore, the results indicate that there are many factors worth studying that affect people's trust in the news. Moreover, the warning sign in communicating machine learning detection results is different from ordinary warnings and needs more detailed research and design. These findings hold important implications for the design of machine learning warnings.

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.HC(Human-Computer Interaction)