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: "mixing local and global information for community detection in large networks"
  Abstract: Clustering networks play a key role in many scientific fields, from Biology to Sociology and Computer Science. Some clustering approaches are called global because they exploit knowledge about the whole network topology. Vice versa, so-called local methods require only a partial knowledge of the network topology. Global approaches yield accurate results but do not scale well on large networks; local approaches, vice versa, are less accurate but computationally fast. We propose CONCLUDE (COmplex Network CLUster DEtection), a new clustering method that couples the accuracy of global approaches with the scalability of local methods. CONCLUDE generates random, non-backtracking walks of finite length to compute the importance of each edge in keeping the network connected, i.e., its edge centrality. Edge centralities allow for mapping vertices onto points of a Euclidean space and compute all-pairs distances between vertices; those distances are then used to partition the network into clusters.

Connected through hyperedge HE-1 (induced by source paper "sorec a social relation based centrality measure in mobile social networks"):
  with members:
    - "d2d big data content deliveries over wireless device to device sharing in large scale mobile networks"
    - "enhancing community detection using a network weighting strategy"
    - "a novel measure of edge centrality in social networks"

Connected through hyperedge HE-2 (induced by source paper "community structure a comparative evaluation of community detection methods"):
  with members:
    - "an empirical characterization of community structures in complex networks using a bivariate map of quality metrics"
    - "efficient method for estimating the number of communities in a network"
    - "the many facets of community detection in complex networks"
    - "the ground truth about metadata and community detection in networks"
    - "z score based modularity for community detection in networks"
    - "metrics for community analysis a survey"
    - "towards linear time overlapping community detection in social networks"
    - "comparative evaluation of community detection algorithms a topological approach"

Connected through hyperedge HE-3 (induced by source paper "engineering parallel algorithms for community detection in massive networks"):
  with members:
    - "fast generation of dynamic complex networks with underlying hyperbolic geometry"
    - "networkit an interactive tool suite for high performance network analysis"
    - "parallel graph partitioning for complex networks"
    - "recent advances in graph partitioning"
    - "on the analysis of a label propagation algorithm for community detection"
    - "defining and evaluating network communities based on ground truth"
    - "static and dynamic aspects of scientific collaboration networks"

Connected through hyperedge HE-4 (induced by source paper "community detection in complex networks using density based clustering algorithm and manifold learning"):
  with members:
    - "community detection in bipartite networks using weighted symmetric binary matrix factorization"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"

Connected through hyperedge HE-5 (induced by source paper "network community detection on metric space"):
  with members:
    - "defining and evaluating network communities based on ground truth"
    - "a classification for community discovery methods in complex networks"
    - "empirical comparison of algorithms for network community detection"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "identifying robust communities and multi community nodes by combining top down and bottom up approaches to clustering"):
  with members:
    - "a new metric for quality of network community structure"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"

Connected through hyperedge HE-7 (induced by source paper "visualizing criminal networks reconstructed from mobile phone records"):
  with members:
    - "detecting criminal organizations in mobile phone networks"
    - "forensic analysis of phone call networks"
    - "enhancing community detection using a network weighting strategy"
    - "a novel measure of edge centrality in social networks"
    - "generalized louvain method for community detection in large networks"

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)