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: "first principles multiway spectral partitioning of graphs"
  Abstract: We consider the minimum-cut partitioning of a graph into more than two parts using spectral methods. While there exist well-established spectral algorithms for this problem that give good results, they have traditionally not been well motivated. Rather than being derived from first principles by minimizing graph cuts, they are typically presented without direct derivation and then proved after the fact to work. In this paper, we take a contrasting approach in which we start with a matrix formulation of the minimum cut problem and then show, via a relaxed optimization, how it can be mapped onto a spectral embedding defined by the leading eigenvectors of the graph Laplacian. The end result is an algorithm that is similar in spirit to, but different in detail from, previous spectral partitioning approaches. In tests of the algorithm we find that it outperforms previous approaches on certain particularly difficult partitioning problems.

Connected through hyperedge HE-1 (induced by source paper "generalized network dismantling"):
  with members:
    - "simulating sir processes on networks using weighted shortest paths"
    - "underestimated cost of targeted attacks on complex networks"
    - "recent advances in graph partitioning"
    - "spin glass approach to the feedback vertex set problem"

Connected through hyperedge HE-2 (induced by source paper "underestimated cost of targeted attacks on complex networks"):
  with members:
    - "the cost of attack in competing networks"
    - "spin glass approach to the feedback vertex set problem"
    - "the role of social networks in information diffusion"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "spectral clustering methods for multiplex networks"):
  with members:
    - "geometric multiscale community detection markov stability and vector partitioning"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "detectability of small communities in multilayer and temporal networks eigenvector localization layer aggregation and time series discretization"
    - "non negative matrix factorizations for multiplex network analysis"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "general tensor spectral co clustering for higher order data"
    - "a new framework for dynamical models on multiplex networks"
    - "network models that reflect multiplex dynamics"

Connected through hyperedge HE-4 (induced by source paper "on spectral partitioning of signed graphs"):
  with members:
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "megaman manifold learning with millions of points"
    - "effective deterministic initialization for k means like methods via local density peaks searching"
    - "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"

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)