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: "brazilian congress structural balance analysis"
  Abstract: In this work, we study the behavior of Brazilian politicians and political parties with the help of clustering algorithms for signed social networks. For this purpose, we extract and analyze a collection of signed networks representing voting sessions of the lower house of Brazilian National Congress. We process all available voting data for the period between 2011 and 2016, by considering voting similarities between members of the Congress to define weighted signed links. The solutions obtained by solving Correlation Clustering (CC) problems are the basis for investigating deputies voting networks as well as questions about loyalty, leadership, coalitions, political crisis, and social phenomena such as mediation and polarization.

Connected through hyperedge HE-1 (induced by source paper "a complex network approach to political analysis application to the brazilian chamber of deputies"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "word sense disambiguation a complex network approach"
    - "extractive multi document summarization using multilayer networks"
    - "analyzing dynamic ideological communities in congressional voting networks"
    - "patterns of authors contribution in scientific manuscripts"
    - "knowledge acquisition a complex networks approach"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "using complex networks for text classification discriminating informative and imaginative documents"

Connected through hyperedge HE-2 (induced by source paper "balance in signed bipartite networks"):
  with members:
    - "butterfly counting in bipartite networks"
    - "signed network modeling based on structural balance theory"
    - "sne signed network embedding"
    - "measuring and modeling bipartite graphs with community structure"
    - "a survey of signed network mining in social media"
    - "common neighbours and the local community paradigm for topological link prediction in bipartite networks"
    - "exploiting the structure of bipartite graphs for algebraic and spectral graph theory applications"
    - "the link prediction problem in bipartite networks"

Connected through hyperedge HE-3 (induced by source paper "multiple partitioning of multiplex signed networks application to european parliament votes"):
  with members:
    - "signed graph analysis for the interpretation of voting behavior"
    - "relevance of negative links in graph partitioning a case study using votes from the european parliament"
    - "generalized measures for the evaluation of community detection methods"
    - "synchronization over z2 and community detection in signed multiplex networks with constraints"
    - "exploiting social network structure for person to person sentiment analysis"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-4 (induced by source paper "analyzing dynamic ideological communities in congressional voting networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "signed graph analysis for the interpretation of voting behavior"
    - "trump vs hillary what went viral during the 2016 us presidential election"
    - "how polarized have we become a multimodal classification of trump followers and clinton followers"
    - "cohesion and coalition formation in the european parliament roll call votes and twitter activities"
    - "how many political parties should brazil have a data driven method to assess and reduce fragmentation in multi party political systems"

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)