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: "generating constrained random graphs using multiple edge switches"
  Abstract: The generation of random graphs using edge swaps provides a reliable method to draw uniformly random samples of sets of graphs respecting some simple constraints, e.g. degree distributions. However, in general, it is not necessarily possible to access all graphs obeying some given con- straints through a classical switching procedure calling on pairs of edges. We therefore propose to get round this issue by generalizing this classical approach through the use of higher-order edge switches. This method, which we denote by "k-edge switching", makes it possible to progres- sively improve the covered portion of a set of constrained graphs, thereby providing an increasing, asymptotically certain confidence on the statistical representativeness of the obtained sample.

Connected through hyperedge HE-1 (induced by source paper "automatic discovery of families of network generative processes"):
  with members:
    - "symbolic regression of generative network models"
    - "evaluating link prediction methods"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-2 (induced by source paper "multiscale planar graph generation"):
  with members:
    - "single and multi level network sparsification by algebraic distance"
    - "generating realistic scaled complex networks"
    - "generating scaled replicas of real world complex networks"
    - "networkit an interactive tool suite for high performance network analysis"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "recent advances in graph partitioning"
    - "community structure and scale free collections of erdős renyi graphs"
    - "advanced coarsening schemes for graph partitioning"

Connected through hyperedge HE-3 (induced by source paper "generating connected random graphs"):
  with members:
    - "super blockers and the effect of network structure on information cascades"
    - "the flip markov chain for connected regular graphs"
    - "geometric inhomogeneous random graphs"

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.SI(Social and Information Networks)