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: "measuring partial balance in signed networks"
  Abstract: Is the enemy of an enemy necessarily a friend? If not, to what extent does this tend to hold? Such questions were formulated in terms of signed (social) networks and necessary and sufficient conditions for a network to be "balanced" were obtained around 1960. Since then the idea that signed networks tend over time to become more balanced has been widely used in several application areas. However, investigation of this hypothesis has been complicated by the lack of a standard measure of partial balance, since complete balance is almost never achieved in practice. We formalize the concept of a measure of partial balance, discuss various measures, compare the measures on synthetic datasets, and investigate their axiomatic properties. The synthetic data involves Erd\H{o}s-R\'enyi and specially structured random graphs. We show that some measures behave better than others in terms of axioms and ability to differentiate between graphs. We also use well-known datasets from the sociology and biology literature, such as Read's New Guinean tribes, gene regulatory networks related to two organisms, and a network involving senate bill co-sponsorship. Our results show that substantially different levels of partial balance is observed under cycle-based, eigenvalue-based, and frustration-based measures. We make some recommendations for measures to be used in future work.

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"
    - "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:
    - "balance and frustration 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 "balance and frustration in signed networks"):
  with members:
    - "computing the line index of balance using integer programming optimisation"
    - "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"
    - "computing the line index of balance using integer programming optimisation"
    - "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 "computing the line index of balance using integer programming optimisation"):
  with members:
    - "balance and frustration 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-6 (induced by source paper "evaluating balance on social networks from their simple cycles"):
  with members:
    - "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"
    - "a modelling and computational study of the frustration index in signed networks"
    - "a polynomial delay algorithm for generating connected induced subgraphs of a given cardinality"
    - "applications of structural balance in signed social networks"

Connected through hyperedge HE-7 (induced by source paper "a modelling and computational study of the frustration index in signed networks"):
  with members:
    - "balance and frustration 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)