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 "node similarity distribution of complex networks and its application in link prediction"):
  Abstract: Over the years, quantifying similarity of nodes has been a hot topic, yet distributions of node similarity for complex networks remain unknown. In this paper, we consider a typical measure called common neighbor based similarity (CNS), which literally characterizes similarity of nodes based on the number of common neighbors (CN) they share in the network. By means of the generating function, we propose a general framework to calculate the distributions of CNS for various complex networks, including the Erdos-Renyi (ER), regular ring lattice, small-world network model, scale-free network model, and real-world networks. In particular, we show that for the ER network, the CNS of node sets with an arbitrary size obeys the Poisson distribution. We also connect the node similarity distribution to the link prediction problem. An interesting finding is that the prediction performance depends solely on the CNS distributions of connected node pairs and unconnected ones. The farther these two CNS distributions are apart, the better the prediction performance is. With these two CNS distributions, we further derive theoretical solutions with respect to two key metrics of prediction performance: i) Precision and ii) area under the receiver operating characteristic curve (AUC), which significantly reduce the evaluation cost of link prediction.

Includes members:
  Member "simplicial closure and higher order link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "measure of node similarity in multilayer networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "phone based metric as a predictor for basic personality traits")
  Member "link prediction in complex networks a mutual information perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review on graph feature learning and feature extraction techniques for 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)