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 "spectral projector based graph fourier transforms"):
  Abstract: This paper considers the definition of the graph Fourier transform (GFT) and of the spectral decomposition of graph signals. Current literature does not address the lack of unicity of the GFT. The GFT is the mapping from the signal set into its representation by a direct sum of irreducible shift invariant subspaces: 1) this   decomposition  may not be unique; and 2) there is freedom in the  choice of basis  for each component subspace. These issues are particularly relevant when the graph shift has repeated eigenvalues as is the case in many real-world applications; by ignoring them, there is no way of knowing if different researchers are using the same definition of the GFT and whether their results are comparable or not. The paper presents how to resolve the above degrees of freedom. We develop a  quasi -coordinate free definition of the GFT and graph spectral decomposition of graph signals that we implement through oblique spectral projectors. We present properties of the GFT and of the spectral projectors and discuss a generalized Parseval's inequality. An illustrative example for a large real-world urban traffic dataset is provided.

Includes members:
  Member "blind identification of graph filters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exact blind community detection from signals on multiple graphs")
  Member "graph equivalence classes for spectral projector based graph fourier transforms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "agile inexact methods for spectral projector based graph fourier transforms")
  Member "autoregressive moving average graph filtering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "signal processing on graphs causal modeling of un structured data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inverse graph learning over optimization networks")
  Member "agile inexact methods for spectral projector based graph fourier transforms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph equivalence classes for spectral projector based graph fourier transforms")
  Member "sampling of graph signals with successive local aggregations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graphbgs background subtraction via recovery of graph signals")
  Member "discrete signal processing on graphs sampling theory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "signal recovery on graphs variation minimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep unsupervised learning of 3d point clouds via graph topology inference and filtering")

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)