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: "expand and contract sampling graphs with given degrees and other combinatorial families"
  Abstract: Sampling from combinatorial families can be difficult. However, complicated families can often be embedded within larger, simpler ones, for which easy sampling algorithms are known. We take advantage of such a relationship to describe a sampling algorithm for the smaller family, via a Markov chain started at a random sample of the larger family. The utility of the method is demonstrated via several examples, with particular emphasis on sampling labelled graphs with given degree sequence, a well-studied problem for which existing algorithms leave much room for improvement. For graphs with given degrees, with maximum degree $O(m^{1/4})$ where $m$ is the number of edges, we obtain an asymptotically uniform sample in $O(m)$ steps, which substantially improves upon existing algorithms.

Connected through hyperedge HE-1 (induced by source paper "the switch markov chain for sampling irregular graphs and digraphs"):
  with members:
    - "a sequential algorithm for generating random graphs"
    - "on uniform sampling simple directed graph realizations of degree sequences"

Connected through hyperedge HE-2 (induced by source paper "i o efficient generation of massive graphs following the lfr benchmark"):
  with members:
    - "the switch markov chain for sampling irregular graphs and digraphs"
    - "node centric detection of overlapping communities in social networks"
    - "analysis of network clustering algorithms and cluster quality metrics at scale"
    - "are we there yet when to stop a markov chain while generating random graphs"
    - "social structure of facebook networks"
    - "the anatomy of the facebook social graph"
    - "fast generation of random connected graphs with prescribed degrees"

Connected through hyperedge HE-3 (induced by source paper "the switch markov chain for sampling irregular graphs"):
  with members:
    - "a sequential algorithm for generating 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.DS(Data Structures and Algorithms)