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 "multiplex graph matching matched filters"):
  Abstract: We consider the problem of detecting a noisy induced multiplex template network in a larger multiplex background network. Our approach, which extends the framework of Sussman et al. (2019) to the multiplex setting, leverages a multiplex analogue of the classical graph matching problem to use the template as a matched filter for efficiently searching the background for candidate template matches. The effectiveness of our approach is demonstrated both theoretically and empirically, with particular attention paid to the potential benefits of considering multiple channels.

Includes members:
  Member "nearly efficient algorithms for the graph matching problem on correlated random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "seedless graph matching via tail of degree distribution for correlated erdos renyi graphs")
  Member "partial recovery of erd h o s r e nyi graph alignment via k core alignment":
    also connected through other hyperedges:
      (none)
  Member "exact alignment recovery for correlated erdos renyi graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privacy of dependent users against statistical matching")
  Member "optimal de anonymization in random graphs with community structure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving privacy in graphs through node addition")
  Member "on the reliability of profile matching across large online social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social 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)