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: "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.

Connected through hyperedge HE-1 (induced by source paper "spectral clustering of signed graphs via matrix power means"):
  with members:
    - "balance in signed networks"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "node classification for signed networks using diffuse interface methods"
    - "signed graph convolutional network"
    - "a modularity based spectral method for simultaneous community and anti community detection"
    - "sne signed network embedding"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "on the troll trust model for edge sign prediction in social networks"

Connected through hyperedge HE-2 (induced by source paper "node classification for signed networks using diffuse interface methods"):
  with members:
    - "spectral clustering of signed graphs via matrix power means"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "community detection in networks via nonlinear modularity eigenvectors"
    - "sne signed network embedding"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "on the troll trust model for edge sign prediction in social networks"
    - "a survey of signed network mining in social media"
    - "vews a wikipedia vandal early warning system"

Connected through hyperedge HE-3 (induced by source paper "practical shape analysis and segmentation methods for point cloud models"):
  with members:
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "inhomogoenous hypergraph clustering with applications"):
  with members:
    - "higher order organization of complex networks"
    - "estimation of kl divergence optimal minimax rate"
    - "scalable motif aware graph clustering"
    - "motif clustering and overlapping clustering for social network analysis"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "learning mixtures of ranking models"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "on the approximation of submodular functions"

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.DS(Data Structures and Algorithms)