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 "on spectral partitioning of signed graphs"):
  Abstract: We argue that the standard graph Laplacian is preferable for spectral partitioning of signed graphs compared to the signed Laplacian. Simple examples demonstrate that partitioning based on signs of components of the leading eigenvectors of the signed Laplacian may be meaningless, in contrast to partitioning based on the Fiedler vector of the standard graph Laplacian for signed graphs. We observe that negative eigenvalues are beneficial for spectral partitioning of signed graphs, making the Fiedler vector easier to compute.

Includes members:
  Member "spectral theory of unsigned and signed graphs applications to graph clustering a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "megaman manifold learning with millions of points":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "preconditioned spectral clustering for stochastic block partition streaming graph challenge preliminary version at arxiv")
  Member "effective deterministic initialization for k means like methods via local density peaks searching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discriminatively boosted image clustering with fully convolutional auto encoders")
  Member "first principles multiway spectral partitioning of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalized network dismantling")
  Member "the hidden convexity of spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "eigenvectors of orthogonally decomposable functions")
  Member "the slashdot zoo mining a social network with negative edges":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep network embedding for graph representation learning in signed networks")
  Member "block locally optimal preconditioned eigenvalue xolvers blopex in hypre and petsc":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "preconditioned spectral clustering for stochastic block partition streaming graph challenge preliminary version at arxiv")
  Member "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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.DS(Data Structures and Algorithms)