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: "lexical convergence and collective identities within facebook echo chambers"
  Abstract: Recent studies, targeting Facebook, showed the tendency of users to interact with information adhering to their preferred narrative and to ignore dissenting information. Primarily driven by confirmation bias, users tend to join homogeneous and polarized clusters (echo chambers) where they cooperate to frame and reinforce a like-minded system of beliefs, thus facilitating misinformation cascades. To gain a deeper understanding of these phenomena, in this work we analyze the lexicons used by the communities of users emerging on Facebook around two very conflicting narratives: science and conspiracy. We show how the words exhibiting a significant differentiation in frequency from one community to another, provide important insights about the kind of information processed by the two groups of users and about the overall sentiment expressed in their comments. Furthermore, by focusing on comment threads, a context of interaction mediated by the posts, we observe a strong positive correlation between the lexical convergence of co-commenters and their number of interactions, both in the case of co-commenters polarized towards the same content as well as in the case of co-commenters with opposing polarization. Nevertheless, the analysis of how lexical convergence evolves through time suggests that such a trend is a proxy of the emergence of collective identities in the case of co-commenters polarized toward the same content, whereas during cross interactions it is more likely due to the need for a common vocabulary to achieve communication than to real opinion convergence. Nonetheless, the fact that even users with opposing views try to coordinate their lexical choices when joining a discussion, suggests that a dialogue between competing parties is possible and indeed it should be stimulated in an attempt to smooth polarization and to reduce both the risk and the consequences of misinformation.

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.SI(Social and Information Networks)