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 "spectre seedless network alignment via spectral centralities"):
  Abstract: Network alignment consists of finding a correspondence between the nodes of two networks. From aligning proteins in computational biology, to de-anonymization of social networks, to recognition tasks in computer vision, this problem has applications in many diverse areas. The current approaches to network alignment mostly focus on the case where prior information is available, either in the form of a seed set of correctly-matched nodes or attributes on the nodes and/or edges. Moreover, those approaches which assume no such prior information tend to be computationally expensive and do not scale to large-scale networks. However, many real-world networks are very large in size, and prior information can be expensive, if not impossible, to obtain. In this paper we introduce SPECTRE, a scalable, accurate algorithm able to solve the network alignment problem with no prior information. SPECTRE makes use of spectral centrality measures and percolation techniques to robustly align nodes across networks, even if those networks exhibit only moderate correlation. Through extensive numerical experiments, we show that SPECTRE is able to recover high-accuracy alignments on both synthetic and real-world networks, and outperforms other algorithms in the seedless case.

Includes members:
  Member "regal representation learning based graph alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep graph matching consensus")
  Member "seeded graph matching via large neighborhood statistics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "seedless graph matching via tail of degree distribution for correlated erdos renyi graphs")
  Member "an iterative global structure assisted labeled network aligner":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "elruna elimination rule based network alignment")
  Member "analysis of a canonical labeling algorithm for the alignment of correlated erd h o s r enyi graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from tree matching to sparse graph alignment")
  Member "gemsec graph embedding with self clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint inference on truth rumor and their sources in social networks")
  Member "exact alignment recovery for correlated erdos renyi graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multiplex graph matching matched filters")
  Member "graph isomorphism in quasipolynomial time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hypergraph isomorphism for groups with restricted composition factors")
  Member "spectral alignment of networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep adversarial network alignment")

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)