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 "detecting coalitions by optimally partitioning signed networks of political collaboration"):
  Abstract: We propose new mathematical programming models for optimal partitioning of a signed graph into cohesive groups. To demonstrate the approach's utility, we apply it to identify coalitions in US Congress since 1979 and examine the impact of polarized coalitions on the effectiveness of passing bills. Our models produce a globally optimal solution to the NP-hard problem of minimizing the total number of intra-group negative and inter-group positive edges. We tackle the intensive computations of dense signed networks by providing upper and lower bounds, then solving an optimization model which closes the gap between the two bounds and returns the optimal partitioning of vertices. Our substantive findings suggest that the dominance of an ideologically homogeneous coalition (i.e. partisan polarization) can be a protective factor that enhances legislative effectiveness.

Includes members:
  Member "balance and frustration in signed networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "signed network structural analysis and applications with a focus on balance theory")
  Member "measuring partial balance in signed networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "signed network structural analysis and applications with a focus on balance theory")
  Member "multiple partitioning of multiplex signed networks application to european parliament votes":
    also connected through other hyperedges:
      (none)
  Member "computing the line index of balance using integer programming optimisation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "signed network structural analysis and applications with a focus on balance theory")
  Member "an exact method for computing the frustration index in signed networks using binary programming":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "signed network structural analysis and applications with a focus on balance theory")
  Member "community detection via semi synchronous label propagation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "embedding based silhouette community detection")

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)