human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "convexity in scientific collaboration networks"):
  Abstract: Abstract   Convexity in a network (graph) has been recently defined as a property of each of its subgraphs to include all shortest paths between the nodes of that subgraph. It can be measured on the scale [0, 1] with 1 being assigned to fully convex networks. The largest convex component of a graph that emerges after the removal of the least number of edges is called a convex skeleton. It is basically a tree of cliques, which has been shown to have many interesting features. In this article the notions of convexity and convex skeletons in the context of scientific collaboration networks are discussed. More specifically, we analyze the co-authorship networks of Slovenian researchers in computer science, physics, sociology, mathematics, and economics and extract convex skeletons from them. We then compare these convex skeletons with the residual graphs (remainders) in terms of collaboration frequency distributions by various parameters such as the publication year and type, co-authors’ birth year, status, gender, discipline, etc. We also show the top-ranked scientists by four basic centrality measures as calculated on the original networks and their skeletons and conclude that convex skeletons may help detect influential scholars that are hardly identifiable in the original collaboration network. As their inherent feature, convex skeletons retain the properties of collaboration networks. These include high-level structural properties but also the fact that the same authors are highlighted by centrality measures. Moreover, the most important ties and thus the most important collaborations are retained in the skeletons.

Includes members:
  Member "convex skeletons of complex networks":
    also connected through other hyperedges:
      (none)
  Member "convexity in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convex skeletons of complex networks")
  Member "constructing bibliometric networks a comparison between full and fractional counting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "who pays comparing cost sharing models for a gold open access publication environment")
  Member "distortive effects of initial based name disambiguation on measurements of large scale coauthorship networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the citation advantage of linking publications to research data")
  Member "full and fractional counting in bibliometric networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on fractional approach to analysis of linked networks")
  Member "betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "does the h α index reinforce the matthew effect in science agent based simulations using stata and r")
  Member "applying centrality measures to impact analysis a coauthorship network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "machine learning in network centrality measures tutorial and outlook")
  Member "co authorship networks in the digital library research community":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "analysis of coauthorship network in political science using centrality measures")

Question: Please tell me which class the source paper 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)