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: "a new metric for quality of network community structure"
  Abstract: Modularity is widely used to effectively measure the strength of the community structure found by community detection algorithms. However, modularity maximization suffers from two opposite yet coexisting problems: in some cases, it tends to favor small communities over large ones while in others, large communities over small ones. The latter tendency is known in the literature as the resolution limit problem. To address them, we propose to modify modularity by subtracting from it the fraction of edges connecting nodes of different communities and by including community density into modularity. We refer to the modified metric as Modularity Density and we demonstrate that it indeed resolves both problems mentioned above. We describe the motivation for introducing this metric by using intuitively clear and simple examples. We also prove that this new metric solves the resolution limit problem. Finally, we discuss the results of applying this metric, modularity, and several other popular community quality metrics to two real dynamic networks. The results imply that Modularity Density is consistent with all the community quality measurements but not modularity, which suggests that Modularity Density is an improved measurement of the community quality compared to modularity.

Connected through hyperedge HE-1 (induced by source paper "a new measure of modularity density for community detection"):
  with members:
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "adaptive modularity maximization via edge weighting scheme"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "enhancing community detection using a network weighting strategy"
    - "efficient community detection in large networks using content and links"
    - "defining and evaluating network communities based on ground truth"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-3 (induced by source paper "evaluation of community structures using kappa index and f score instead of normalized mutual information"):
  with members:
    - "defining and evaluating network communities based on ground truth"
    - "bad communities with high modularity"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-4 (induced by source paper "the many facets of community detection in complex networks"):
  with members:
    - "modelling sequences and temporal networks with dynamic community structures"
    - "the ground truth about metadata and community detection in networks"
    - "maps of sparse markov chains efficiently reveal community structure in network flows with memory"
    - "higher order organization of complex networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "clustering and community detection in directed networks a survey"

Connected through hyperedge HE-5 (induced by source paper "different approaches to community detection"):
  with members:
    - "modelling sequences and temporal networks with dynamic community structures"
    - "the ground truth about metadata and community detection in networks"
    - "higher order organization of complex networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "clustering and community detection in directed networks a survey"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-6 (induced by source paper "permanence and community structure in complex networks"):
  with members:
    - "genperm a unified method for detecting non overlapping and overlapping communities"
    - "leveraging disjoint communities for detecting overlapping community structure"
    - "community detection and graph partitioning"
    - "enhancing community detection using a network weighting strategy"
    - "discovering link communities in complex networks by exploiting link dynamics"
    - "towards linear time overlapping community detection in social networks"
    - "comparative evaluation of community detection algorithms a topological approach"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-7 (induced by source paper "fuzzy overlapping community quality metrics"):
  with members:
    - "identifying robust communities and multi community nodes by combining top down and bottom up approaches to clustering"
    - "extension of modularity density for overlapping community structure"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "towards linear time overlapping community detection in social networks"
    - "defining and evaluating network communities based on ground truth"
    - "detect overlapping and hierarchical community structure in networks"

Connected through hyperedge HE-8 (induced by source paper "identifying robust communities and multi community nodes by combining top down and bottom up approaches to clustering"):
  with members:
    - "mixing local and global information for community detection in large networks"
    - "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"

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)