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 of 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 by these scores. 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 100 Facebook social networks with respect to network properties including diameter, connected components, community structure, and multiple node centrality measures. Experiments with our implementations of the sparsification methods (using 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. Furthermore, the experimental results allow us to differentiate the behavior of different methods and show which method is suitable with respect to which property. Our Local Degree method is fast enough for large-scale networks and performs well across a wider range of properties than previously proposed methods.

Connected through hyperedge HE-1 (induced by source paper "single and multi level network sparsification by algebraic distance"):
  with members:
    - "networkit an interactive tool suite for high performance network analysis"
    - "a survey and taxonomy of graph sampling"
    - "discovering social circles in ego networks"
    - "network sampling from static to streaming graphs"
    - "large social networks can be targeted for viral marketing with small seed sets"
    - "advanced coarsening schemes for graph partitioning"
    - "relaxation based coarsening and multiscale graph organization"
    - "spectral sparsification of graphs"

Connected through hyperedge HE-2 (induced by source paper "uncertain graph sparsification"):
  with members:
    - "on fully dynamic graph sparsifiers"
    - "simple parallel and distributed algorithms for spectral graph sparsification"
    - "single pass spectral sparsification in dynamic streams"
    - "mining maximal cliques from an uncertain graph"
    - "improved parallel algorithms for spanners and hopsets"
    - "distance preserving graph simplification"
    - "randomized approximation schemes for cuts and flows in capacitated graphs"

Connected through hyperedge HE-3 (induced by source paper "structure preserving sparsification methods for social networks"):
  with members:
    - "engineering parallel algorithms for community detection in massive networks"
    - "single and multi level network sparsification by algebraic distance"
    - "networkit a tool suite for large scale complex network analysis"
    - "network sampling from static to streaming graphs"
    - "social structure of facebook networks"
    - "defining and evaluating network communities based on ground truth"

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)