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: "encouraging diversity and representation awareness in geographically centralized content"
  Abstract: In centralized countries, not only population, media and economic power are concentrated, but people give more attention to central locations. While this is not inherently bad, this behavior extends to micro-blogging platforms: central locations get more attention in terms of information flow. In this paper we study the effects of an information filtering algorithm that decentralizes content in such platforms. Particularly, we found that users from non-central locations were not able to identify the geographical diversity on timelines generated by the algorithm, which were diverse by construction. To make users see the inherent diversity, we define a design rationale to approach this problem, focused on the treemap visualization technique. Then, we deployed an" in the wild" implementation of our proposed system. On one hand, we found that there are effects of centralization in exploratory user behavior. On the other hand, we found that the treemap was able to make users see the inherent geographical diversity of timelines. We measured these effects based on how users engaged with content filtered by the algorithm. With these results in mind, we propose practical actions for micro-blogging platforms to account for the differences and biased behavior induced by centralization.

Connected through hyperedge HE-1 (induced by source paper "characterization of local attitudes toward immigration using social media"):
  with members:
    - "topic supervised non negative matrix factorization"
    - "finding intermediary topics between people of opposing views a case study"

Connected through hyperedge HE-2 (induced by source paper "data portraits and intermediary topics encouraging exploration of politically diverse profiles"):
  with members:
    - "finding intermediary topics between people of opposing views a case study"
    - "data portraits connecting people of opposing views"

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)