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: "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
  Abstract: This is a survey of the method of graph cuts and its applications to graph clustering of weighted unsigned and signed graphs. I provide a fairly thorough treatment of the method of normalized graph cuts, a deeply original method due to Shi and Malik, including complete proofs. The main thrust of this paper is the method of normalized cuts. I give a detailed account for K = 2 clusters, and also for K > 2 clusters, based on the work of Yu and Shi. I also show how both graph drawing and normalized cut K-clustering can be easily generalized to handle signed graphs, which are weighted graphs in which the weight matrix W may have negative coefficients. Intuitively, negative coefficients indicate distance or dissimilarity. The solution is to replace the degree matrix by the matrix in which absolute values of the weights are used, and to replace the Laplacian by the Laplacian with the new degree matrix of absolute values. As far as I know, the generalization of K-way normalized clustering to signed graphs is new. Finally, I show how the method of ratio cuts, in which a cut is normalized by the size of the cluster rather than its volume, is just a special case of normalized cuts.

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"
    - "on spectral partitioning of signed graphs"
    - "a modularity based spectral method for simultaneous community and anti community detection"
    - "sne signed network embedding"
    - "on the troll trust model for edge sign prediction in social networks"

Connected through hyperedge HE-2 (induced by source paper "signed network structural analysis and applications with a focus on balance theory"):
  with members:
    - "balance and frustration in signed networks"
    - "measuring partial balance in signed networks"
    - "computing the line index of balance using integer programming optimisation"
    - "evaluating balance on social networks from their simple cycles"
    - "a general purpose algorithm for counting simple cycles and simple paths of any length"
    - "an exact method for computing the frustration index in signed networks using binary programming"
    - "powers of tensors and fast matrix multiplication"
    - "applications of structural balance in signed social networks"

Connected through hyperedge HE-3 (induced by source paper "an mbo scheme for clustering and semi supervised clustering of signed networks"):
  with members:
    - "node classification for signed networks using diffuse interface methods"
    - "a survey of signed network mining in social media"
    - "a method based on total variation for network modularity optimization using the mbo scheme"
    - "notes on elementary spectral graph theory applications to graph clustering using normalized cuts"
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "extracting spatial information from networks with low order eigenvectors"
    - "max cut and the smallest eigenvalue"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (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"
    - "on spectral partitioning of signed graphs"
    - "sne signed network embedding"
    - "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-5 (induced by source paper "measuring partial balance in signed networks"):
  with members:
    - "evaluating balance on social networks from their simple cycles"
    - "a general purpose algorithm for counting simple cycles and simple paths of any length"
    - "an exact method for computing the frustration index in signed networks using binary programming"
    - "applications of structural balance in signed social networks"

Connected through hyperedge HE-6 (induced by source paper "on spectral partitioning of signed graphs"):
  with members:
    - "megaman manifold learning with millions of points"
    - "effective deterministic initialization for k means like methods via local density peaks searching"
    - "first principles multiway spectral partitioning of graphs"
    - "the hidden convexity of spectral clustering"
    - "the slashdot zoo mining a social network with negative edges"
    - "block locally optimal preconditioned eigenvalue xolvers blopex in hypre and petsc"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "seating assignment using constrained signed spectral clustering"):
  with members:
    - "semantic word clusters using signed normalized graph cuts"
    - "a survey of signed network mining in social media"

Connected through hyperedge HE-8 (induced by source paper "semantic word clusters using signed normalized graph cuts"):
  with members:
    - "compositional distributional semantics with long short term memory"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "from frequency to meaning vector space models of semantics"

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.LG(Machine Learning)