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: "generalized measures for the evaluation of community detection methods"
  Abstract: Community detection can be considered as a variant of cluster analysis applied to complex networks. For this reason, all existing studies have been using tools derived from this field when evaluating community detection algorithms. However, those are not completely relevant in the context of network analysis, because they ignore an essential part of the available information: the network structure. Therefore, they can lead to incorrect interpretations. In this article, we review these measures, and illustrate this limitation. We propose a modification to solve this problem, and apply it to the three most widespread measures: purity, Rand index and normalized mutual information (NMI). We then perform an experimental evaluation on artificially generated networks with realistic community structure. We assess the relevance of the modified measures by comparison with their traditional counterparts, and also relatively to the topological properties of the community structures. On these data, the modified NMI turns out to provide the most relevant results.

Connected through hyperedge HE-1 (induced by source paper "efficient community detection in boolean composed multiplex networks"):
  with members:
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "modularity in complex multilayer networks with multiple aspects a static perspective"
    - "networkit an interactive tool suite for high performance network analysis"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "clustering with multi layer graphs a spectral perspective"

Connected through hyperedge HE-2 (induced by source paper "multiple partitioning of multiplex signed networks application to european parliament votes"):
  with members:
    - "signed graph analysis for the interpretation of voting behavior"
    - "brazilian congress structural balance analysis"
    - "relevance of negative links in graph partitioning a case study using votes from the european parliament"
    - "synchronization over z2 and community detection in signed multiplex networks with constraints"
    - "exploiting social network structure for person to person sentiment analysis"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-3 (induced by source paper "metrics for community analysis a survey"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "higher order organization of complex networks"
    - "genperm a unified method for detecting non overlapping and overlapping communities"
    - "z score based modularity for community detection in networks"
    - "permanence and community structure in complex networks"
    - "generalization of clustering agreements and distances for overlapping clusters and network communities"
    - "leveraging disjoint communities for detecting overlapping community structure"

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)