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: "applications of structural balance in signed social networks"
  Abstract: We present measures, models and link prediction algorithms based on the structural balance in signed social networks. Certain social networks contain, in addition to the usual 'friend' links, 'enemy' links. These networks are called signed social networks. A classical and major concept for signed social networks is that of structural balance, i.e., the tendency of triangles to be 'balanced' towards including an even number of negative edges, such as friend-friend-friend and friend-enemy-enemy triangles. In this article, we introduce several new signed network analysis methods that exploit structural balance for measuring partial balance, for finding communities of people based on balance, for drawing signed social networks, and for solving the problem of link prediction. Notably, the introduced methods are based on the signed graph Laplacian and on the concept of signed resistance distances. We evaluate our methods on a collection of four signed social network datasets.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "balance and frustration in signed networks"):
  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"
    - "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"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-3 (induced by source paper "measuring partial balance in signed networks"):
  with members:
    - "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"

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

Connected through hyperedge HE-5 (induced by source paper "evaluating balance on social networks from their simple cycles"):
  with members:
    - "measuring partial balance in signed networks"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "eigenvalues of weakly balanced signed graphs and graphs with negative cliques"):
  with members:
    - "on characteristic and permanent polynomials of a matrix"

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"
    - "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"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-8 (induced by source paper "a survey of signed network mining in social media"):
  with members:
    - "signed link analysis in social media networks"
    - "stochastic block model and exploratory analysis in signed networks"
    - "signed graph embedding when everybody can sit closer to friends than enemies"
    - "overlapping community detection in networks with positive and negative links"
    - "negative link prediction in social media"
    - "breaking the small cluster barrier of graph clustering"
    - "prediction and clustering in signed networks a local to global perspective"
    - "sitting closer to friends than enemies revisited"

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)