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 "computing the line index of balance using integer programming optimisation"):
  Abstract: An important measure of signed graphs is the line index of balance which has applications in many fields. However, this graph-theoretic measure was underused for decades because of the inherent complexity in its computation which is closely related to solving NP-hard graph optimisation problems like MAXCUT. We develop new quadratic and linear programming models to compute the line index of balance exactly. Using the Gurobi integer programming optimisation solver, we evaluate the line index of balance on real-world and synthetic datasets. The synthetic data involves Erdős-Renyi graphs, Barabasi-Albert graphs, and specially structured random graphs. We also use well-known datasets from the sociology literature, such as signed graphs inferred from students’ choice and rejection, as well as datasets from the biology literature including gene regulatory networks. The results show that exact values of the line index of balance in relatively large signed graphs can be efficiently computed using our suggested optimisation models. We find that most real-world social networks and some biological networks have a small line index of balance which indicates that they are close to balanced.

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 "an exact method for computing the frustration index in signed networks using binary programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "detecting coalitions by optimally partitioning signed networks of political collaboration")
  Member "a modelling and computational study of the frustration index in signed networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generating weighted max 2 sat instances of tunable difficulty with frustrated loops")
  Member "applications of structural balance in signed social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "signed network structural analysis and applications with a focus on balance theory")

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)