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 domain sampling of graph signals"):
  Abstract: Sampling methods for graph signals in the graph spectral domain are presented. Though the conventional sampling of graph signals can be regarded as sampling in the graph vertex domain, it does not have the desired characteristics in regard to the graph spectral domain. With the proposed methods, the down- and upsampled graph signals inherit the frequency-domain characteristics of the sampled signals defined in the time/spatial domain. The properties of the sampling effects were evaluated theoretically in comparison with those obtained with the conventional sampling method in the vertex domain. Various examples of signals on simple graphs enable precise understanding of the problem considered. Fractional sampling and Laplacian pyramid representation of graph signals are potential applications of these methods.

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 "spectral projector based graph fourier transforms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topology adaptive graph convolutional networks")
  Member "graph laplacian regularization for image denoising analysis in the continuous domain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast graph metric learning via gershgorin disc alignment")
  Member "random walk graph laplacian based smoothness prior for soft decoding of jpeg images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "stationary signal processing on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph")
  Member "fast robust pca on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph")
  Member "efficient sampling set selection for bandlimited graph signals using graph spectral proxies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "edgenets edge varying graph neural networks")
  Member "an m channel critically sampled filter bank for graph signals":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scalable m channel critically sampled filter banks for graph signals")

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.IT(Information Theory)