human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "analyzing dynamic ideological communities in congressional voting networks"):
  Abstract: We here study the behavior of political party members aiming at identifying how ideological communities are created and evolve over time in diverse (fragmented and non-fragmented) party systems. Using public voting data of both Brazil and the US, we propose a methodology to identify and characterize ideological communities, their member polarization, and how such communities evolve over time, covering a 15-year period. Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties.

Includes members:
  Member "community discovery in dynamic networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of community search over big graphs")
  Member "signed graph analysis for the interpretation of voting behavior":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "signed graph attention networks")
  Member "trump vs hillary what went viral during the 2016 us presidential election":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantifying polarization on twitter the kavanaugh nomination")
  Member "how polarized have we become a multimodal classification of trump followers and clinton followers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fairface face attribute dataset for balanced race gender and age")
  Member "cohesion and coalition formation in the european parliament roll call votes and twitter activities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a complex network approach to political analysis application to the brazilian chamber of deputies")
  Member "brazilian congress structural balance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a complex network approach to political analysis application to the brazilian chamber of deputies")
  Member "how many political parties should brazil have a data driven method to assess and reduce fragmentation in multi party political systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when politicians talk about politics identifying political tweets of brazilian congressmen")

Question: Please tell me which class the source paper 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)