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 "when centrality measures deceive us"):
  Abstract: Matrix-based centrality measures have enjoyed significant popularity in network analysis, in no small part due to our ability to rigorously analyze their behavior as parameters vary. One desirable property of a good centrality measure is that structurally equivalent nodes are assigned the same score (and dissimilar nodes are not). We show that when nodes are characterized into walk classes according to the number of incident $\ell$-walks for all $\ell > 0$, even well-studied centrality measures can be "deceived" into assigning nodes with different walk structure the same score. Further, there are infinitely many parameter values and graphs on which these measures cannot distinguish nodes from distinct walk classes, and even the exponential function can produce uniform centrality scores on non--walk-regular graphs. Not all hope is lost, however, as we show that a broad class of functions including the matrix exponential and resolvent cannot be deceived for every parameter value in any set with a limit point.

Includes members:
  Member "pagerank beyond the web":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nonlinearity networks a 2020 vision")
  Member "about the discriminant power of the subgraph centrality and other centrality measures about the discriminant power of the subgraph centrality and other centrality measures working paper":
    also connected through other hyperedges:
      (none)
  Member "total communicability as a centrality measure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlocal pagerank")

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)