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 "spectral clustering of signed graphs via matrix power means"):
  Abstract: Signed graphs encode positive (attractive) and negative (repulsive) relations between nodes. We extend spectral clustering to signed graphs via the one-parameter family of Signed Power Mean Laplacians, defined as the matrix power mean of normalized standard and signless Laplacians of positive and negative edges. We provide a thorough analysis of the proposed approach in the setting of a general Stochastic Block Model that includes models such as the Labeled Stochastic Block Model and the Censored Block Model. We show that in expectation the signed power mean Laplacian captures the ground truth clusters under reasonable settings where state-of-the-art approaches fail. Moreover, we prove that the eigenvalues and eigenvector of the signed power mean Laplacian concentrate around their expectation under reasonable conditions in the general Stochastic Block Model. Extensive experiments on random graphs and real world datasets confirm the theoretically predicted behaviour of the signed power mean Laplacian and show that it compares favourably with state-of-the-art methods.

Includes members:
  Member "balance in signed networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "community detectability and structural balance dynamics in signed networks")
  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 "node classification for signed networks using diffuse interface methods")
  Member "node classification for signed networks using diffuse interface methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an mbo scheme for clustering and semi supervised clustering of signed networks")
  Member "signed graph convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "signed graph attention networks")
  Member "on spectral partitioning of signed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "node classification for signed networks using diffuse interface methods")
  Member "a modularity based spectral method for simultaneous community and anti community detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nonlocal pagerank")
  Member "sne signed network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "signed network structural analysis and applications with a focus on balance theory")

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.LG(Machine Learning)