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: "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.

Connected through hyperedge HE-1 (induced by source paper "detecting coalitions by optimally partitioning signed networks of political collaboration"):
  with members:
    - "balance and frustration in signed networks"
    - "measuring partial balance in signed networks"
    - "multiple partitioning of multiplex signed networks application to european parliament votes"
    - "an exact method for computing the frustration index in signed networks using binary programming"
    - "community detection via semi synchronous label propagation algorithms"

Connected through hyperedge HE-2 (induced by source paper "signed network structural analysis and applications with a focus on balance theory"):
  with members:
    - "balance and frustration in signed networks"
    - "measuring partial balance in signed networks"
    - "evaluating balance on social networks from their simple cycles"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "a general purpose algorithm for counting simple cycles and simple paths of any length"
    - "an exact method for computing the frustration index in signed networks using binary programming"
    - "powers of tensors and fast matrix multiplication"
    - "applications of structural balance in signed social networks"

Connected through hyperedge HE-3 (induced by source paper "balance and frustration in signed networks"):
  with members:
    - "measuring partial balance in signed networks"
    - "evaluating balance on social networks from their simple cycles"
    - "a general purpose algorithm for counting simple cycles and simple paths of any length"
    - "an exact method for computing the frustration index in signed networks using binary programming"
    - "applications of structural balance in signed social networks"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-4 (induced by source paper "visualizing structural balance in signed networks"):
  with members:
    - "discovering polarized communities in signed networks"
    - "reconciliation k median clustering with non polarized representatives"
    - "measuring partial balance in signed networks"
    - "structural balance and opinion separation in trust mistrust social networks"
    - "a survey of signed network mining in social media"

Connected through hyperedge HE-5 (induced by source paper "a modelling and computational study of the frustration index in signed networks"):
  with members:
    - "balance and frustration in signed networks"
    - "measuring partial balance in signed networks"
    - "relevance of negative links in graph partitioning a case study using votes from the european parliament"
    - "powers of tensors and fast matrix multiplication"
    - "applications of structural balance in signed social networks"
    - "correlation clustering with a fixed number of clusters"

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)