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 "rank me thou shalln t compare me"):
  Abstract: Centrality measures have been defined to quantify the importance of a node in complex networks. The relative importance of a node can be measured using its centrality rank based on the centrality value. In the present work, we predict the degree centrality rank of a node without having the entire network. The proposed method uses degree of the node and some network parameters to predict its rank. These network parameters include network size, minimum, maximum, and average degree of the network. These parameters are estimated using random walk sampling techniques. The proposed method is validated on Barabasi-Albert networks. Simulation results show that the proposed method predicts the rank of higher degree nodes with more accuracy. The average error in the rank prediction is approximately $0.16\%$ of the network size.

Includes members:
  Member "modeling memetics using edge diversity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimating shell index in a graph with local information")
  Member "evolving models for meso scale structures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimating shell index in a graph with local information")
  Member "understanding spreading patterns on social networks based on network topology":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating shell index in a graph with local information")
  Member "estimating the degree centrality ranking of a node":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "estimating shell index in a graph with local information")
  Member "graph size estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "true nonlinear dynamics from incomplete networks")

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)