human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "node classification for signed networks using diffuse interface methods"):
  Abstract: Signed networks contain both positive and negative kinds of interactions like friendship and enmity. The task of node classification in non-signed graphs has proven to be beneficial in many real world applications, yet extensions to signed networks remain largely unexplored. In this paper we introduce the first analysis of node classification in signed social networks via diffuse interface methods based on the Ginzburg-Landau functional together with different extensions of the graph Laplacian to signed networks. We show that blending the information from both positive and negative interactions leads to performance improvement in real signed social networks, consistently outperforming the current state of the art.

Includes members:
  Member "spectral clustering of signed graphs via matrix power means":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalized matrix means for semi supervised learning with multilayer graphs")
  Member "an mbo scheme for clustering and semi supervised clustering of signed networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "community detection in networks via nonlinear modularity eigenvectors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic block models are a discrete surface tension")
  Member "on spectral partitioning of signed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "sne signed network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable graph embeddings via sparse transpose proximities")
  Member "spectral theory of unsigned and signed graphs applications to graph clustering a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "on the troll trust model for edge sign prediction in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "a survey of signed network mining in social media":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")

Question: Please tell me which class the source paper 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)