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 "a modelling and computational study of the frustration index in signed networks"):
  Abstract: Computing the frustration index of a signed graph is a key step toward solving problems in many fields including social networks, political science, physics, chemistry, and biology. The frustration index determines the distance of a network from a state of total structural balance. Although the definition of the frustration index goes back to the 1950's, its exact algorithmic computation, which is closely related to classic NP-hard graph problems, has only become a focus in recent years. We develop three new binary linear programming models to compute the frustration index exactly and efficiently as the solution to a global optimisation problem. Solving the models with prioritised branching and valid inequalities in Gurobi, we can compute the frustration index of real signed networks with over 15000 edges in less than a minute on inexpensive hardware. We provide extensive performance analysis for both random and real signed networks and show that our models outperform all existing approaches by large factors. Based on solve time, algorithm output, and effective branching factor we highlight the superiority of our models to both exact and heuristic methods in the literature.

Includes members:
  Member "balance and frustration in signed networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "detecting coalitions by optimally partitioning signed networks of political collaboration")
  Member "measuring partial balance in signed networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "detecting coalitions by optimally partitioning signed networks of political collaboration")
  Member "computing the line index of balance using integer programming optimisation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "detecting coalitions by optimally partitioning signed networks of political collaboration")
  Member "relevance of negative links in graph partitioning a case study using votes from the european parliament":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiple partitioning of multiplex signed networks application to european parliament votes")
  Member "powers of tensors and fast matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "applications of structural balance in signed social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "signed network structural analysis and applications with a focus on balance theory")
  Member "correlation clustering with a fixed number of clusters":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spectral clustering of signed graphs via matrix power means")

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)