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: "an analysis of centrality measures for complex and social networks"
  Abstract: Measures of complex network analysis, such as vertex centrality, have the potential to unveil existing network patterns and behaviors. They contribute to the understanding of networks and their components by analyzing their structural properties, which makes them useful in several computer science domains and applications. Unfortunately, there is a large number of distinct centrality measures and little is known about their common characteristics in practice. By means of an empirical analysis, we aim at a clear understanding of the main centrality measures available, unveiling their similarities and differences in a large number of distinct social networks. Our experiments show that the vertex centrality measures known as information, eigenvector, subgraph, walk betweenness and betweenness can distinguish vertices in all kinds of networks with a granularity performance at 95%, while other metrics achieved a considerably lower result. In addition, we demonstrate that several pairs of metrics evaluate the vertices in a very similar way, i.e. their correlation coefficient values are above 0.7. This was unexpected, considering that each metric presents a quite distinct theoretical and algorithmic foundation. Our work thus contributes towards the development of a methodology for principled network analysis and evaluation.

Connected through hyperedge HE-1 (induced by source paper "novel machine learning algorithms for centrality and cliques detection in youtube social networks"):
  with members:
    - "computing vertex centrality measures in massive real networks with a neural learning model"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "machine learning in network centrality measures tutorial and outlook"):
  with members:
    - "a scalable generative graph model with community structure"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "computing classic closeness centrality at scale"
    - "efficient computation of the shapley value for game theoretic network centrality"
    - "representation learning a review and new perspectives"
    - "community structure and scale free collections of erdős renyi graphs"
    - "defining and evaluating network communities based on ground truth"
    - "applying centrality measures to impact analysis a coauthorship network analysis"

Connected through hyperedge HE-3 (induced by source paper "computing vertex centrality measures in massive real networks with a neural learning model"):
  with members:
    - "a scalable generative graph model with community structure"
    - "community structure and scale free collections of erdős renyi graphs"
    - "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)