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: "structure preserving sparsification methods for social networks"
  Abstract: Sparsification reduces the size of networks while preserving structural and statistical properties of interest. Various sparsifying algorithms have been proposed in different contexts. We contribute the first systematic conceptual and experimental comparison of \textit{edge sparsification} methods on a diverse set of network properties. It is shown that they can be understood as methods for rating edges by importance and then filtering globally or locally by these scores. We show that applying a local filtering technique improves the preservation of all kinds of properties. In addition, we propose a new sparsification method (\textit{Local Degree}) which preserves edges leading to local hub nodes. All methods are evaluated on a set of social networks from Facebook, Google+, Twitter and LiveJournal with respect to network properties including diameter, connected components, community structure, multiple node centrality measures and the behavior of epidemic simulations. In order to assess the preservation of the community structure, we also include experiments on synthetically generated networks with ground truth communities. Experiments with our implementations of the sparsification methods (included in the open-source network analysis tool suite NetworKit) show that many network properties can be preserved down to about 20\% of the original set of edges for sparse graphs with a reasonable density. The experimental results allow us to differentiate the behavior of different methods and show which method is suitable with respect to which property. While our Local Degree method is best for preserving connectivity and short distances, other newly introduced local variants are best for preserving the community structure.

Connected through hyperedge HE-1 (induced by source paper "convex skeletons of complex networks"):
  with members:
    - "convexity in complex networks"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "the many facets of community detection in complex networks"
    - "a stopping criterion for markov chains when generating independent random graphs"

Connected through hyperedge HE-2 (induced by source paper "generating realistic scaled complex networks"):
  with members:
    - "engineering parallel algorithms for community detection in massive networks"
    - "snap a general purpose network analysis and graph mining library"
    - "generating scaled replicas of real world complex networks"
    - "drawing large graphs by multilevel maxent stress optimization"
    - "networkit a tool suite for large scale complex network analysis"
    - "a scalable generative graph model with community structure"
    - "tree based coarsening and partitioning of complex networks"
    - "recent advances in graph partitioning"

Connected through hyperedge HE-3 (induced by source paper "generating scaled replicas of real world complex networks"):
  with members:
    - "engineering parallel algorithms for community detection in massive networks"
    - "networkit a tool suite for large scale complex network analysis"
    - "a scalable generative graph model with community structure"
    - "social structure of facebook networks"
    - "multiscale network generation"

Connected through hyperedge HE-4 (induced by source paper "networkit a tool suite for large scale complex network analysis"):
  with members:
    - "querying probabilistic neighborhoods in spatial data sets efficiently"
    - "engineering parallel algorithms for community detection in massive networks"
    - "snap a general purpose network analysis and graph mining library"
    - "fully dynamic approximation of betweenness centrality"
    - "approximating betweenness centrality in large evolving networks"
    - "social structure of facebook networks"
    - "the similarity between stochastic kronecker and chung lu graph models"
    - "fast computation of empirically tight bounds for the diameter of massive 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)