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: "signed graph analysis for the interpretation of voting behavior"
  Abstract: In a signed graph, each link is labeled with either a positive or a negative sign. This is particularly appropriate to model polarized systems. Such a graph can be characterized through the notion of structural balance, which relies on the partitioning of the graph into internally solidary but mutually hostile subgroups. In this work, we show that signed graphs can be used to model and understand voting behavior. We take advantage of data from the European Parliament to confront two variants of structural balance, and illustrate how their use can help better understanding the studied system.

Connected through hyperedge HE-1 (induced by source paper "signed graph attention networks"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "signed graph convolutional network"
    - "neural message passing for quantum chemistry"
    - "sne signed network embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "multiple partitioning of multiplex signed networks application to european parliament votes"):
  with members:
    - "brazilian congress structural balance analysis"
    - "relevance of negative links in graph partitioning a case study using votes from the european parliament"
    - "generalized measures for the evaluation of community detection methods"
    - "synchronization over z2 and community detection in signed multiplex networks with constraints"
    - "exploiting social network structure for person to person sentiment analysis"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-3 (induced by source paper "analyzing dynamic ideological communities in congressional voting networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "trump vs hillary what went viral during the 2016 us presidential election"
    - "how polarized have we become a multimodal classification of trump followers and clinton followers"
    - "cohesion and coalition formation in the european parliament roll call votes and twitter activities"
    - "brazilian congress structural balance analysis"
    - "how many political parties should brazil have a data driven method to assess and reduce fragmentation in multi party political systems"

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)