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: "balance and frustration in signed networks"
  Abstract: The frustration index is a key measure for analysing signed networks, which has been underused due to its computational complexity. We use an exact optimisation-based method to analyse frustration as a global structural property of signed networks coming from diverse application areas. In the classic friend-enemy interpretation of balance theory, a by-product of computing the frustration index is the partitioning of nodes into two internally solidary but mutually hostile groups. #R##N#The main purpose of this paper is to present general methodology for answering questions related to partial balance in signed networks, and apply it to a range of representative examples that are now analysable because of advances in computational methods. #R##N#We provide exact numerical results on social and biological signed networks, networks of formal alliances and antagonisms between countries, and financial portfolio networks. Molecular graphs of carbon and Ising models are also considered. We point out several mistakes in the signed networks literature caused by inaccurate computation, implementation errors or inappropriate measures.

Connected through hyperedge HE-1 (induced by source paper "detecting coalitions by optimally partitioning signed networks of political collaboration"):
  with members:
    - "measuring partial balance in signed networks"
    - "multiple partitioning of multiplex signed networks application to european parliament votes"
    - "computing the line index of balance using integer programming optimisation"
    - "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:
    - "measuring partial balance in signed networks"
    - "computing the line index of balance using integer programming optimisation"
    - "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 "computing the line index of balance using integer programming optimisation"):
  with members:
    - "measuring partial balance in signed networks"
    - "an exact method for computing the frustration index in signed networks using binary programming"
    - "a modelling and computational study of the frustration index in signed networks"
    - "applications of structural balance in signed social networks"

Connected through hyperedge HE-4 (induced by source paper "a modelling and computational study of the frustration index in signed networks"):
  with members:
    - "measuring partial balance in signed networks"
    - "computing the line index of balance using integer programming optimisation"
    - "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)