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: "single and multi level network sparsification by algebraic distance"
  Abstract: Network sparsification methods play an important role in modern network analysis when fast estimation of computationally expensive properties (such as the diameter, centrality indices, and paths) is required. We propose a method of network sparsification that preserves a wide range of structural properties. Depending on the analysis goals, the method allows to distinguish between local and global range edges that can be filtered out during the sparsification. First we rank edges by their algebraic distances and then we sample them. We also introduce a multilevel framework for sparsification that can be used to control the sparsification process at various coarse-grained resolutions. Based primarily on the matrix-vector multiplications, our method is easily parallelized for different architectures.

Connected through hyperedge HE-1 (induced by source paper "fobe and hobe first and high order bipartite embeddings"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "relaxation based coarsening for multilevel hypergraph partitioning"
    - "node2vec scalable feature learning for networks"
    - "tensorflow a system for large scale machine learning"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "multiscale planar graph generation"):
  with members:
    - "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"
    - "multiscale network generation"

Connected through hyperedge HE-3 (induced by source paper "a framework for analyzing resparsification algorithms"):
  with members:
    - "online row sampling"
    - "on sketching quadratic forms"
    - "simple parallel and distributed algorithms for spectral graph sparsification"
    - "parallel graph decompositions using random shifts"
    - "improved parallel algorithms for spanners and hopsets"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"
    - "towards an sdp based approach to spectral methods a nearly linear time algorithm for graph partitioning and decomposition"

Connected through hyperedge HE-4 (induced by source paper "structure preserving sparsification methods for social networks"):
  with members:
    - "engineering parallel algorithms for community detection in massive networks"
    - "structure preserving sparsification of social networks"
    - "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)