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 "link prediction in social networks the state of the art"):
  Abstract: In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed.

Includes members:
  Member "link prediction in complex networks a clustering perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance of local information based link prediction a sampling perspective")
  Member "nonparametric link prediction in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dyngraph2vec capturing network dynamics using dynamic graph representation learning")
  Member "predictors of short term decay of cell phone contacts in a large scale communication network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "influence of personal preferences on link dynamics in social networks")
  Member "supervised random walks predicting and recommending links in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures")
  Member "the link prediction problem in bipartite networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bipartite link prediction based on topological features via 2 hop path")
  Member "role of weak ties in link prediction of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "leveraging network dynamics for improved link prediction")

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)