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 "signal representations on graphs tools and applications"):
  Abstract: We present a framework for representing and modeling data on graphs. Based on this framework, we study three typical classes of graph signals: smooth graph signals, piecewise-constant graph signals, and piecewise-smooth graph signals. For each class, we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties. We then study how such graph dictionary works in two standard tasks: approximation and sampling followed with recovery, both from theoretical as well as algorithmic perspectives. Finally, for each class, we present a case study of a real-world problem by using the proposed methodology.

Includes members:
  Member "reconstruction of graph signals through percolation from seeding nodes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "edgenets edge varying graph neural networks")
  Member "constrained adaptive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "signals on graphs uncertainty principle and sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "signal recovery on graphs random versus experimentally designed sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "active learning for graph neural networks via node feature propagation")
  Member "local set based graph signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graph signal processing part ii processing and analyzing signals on graphs")
  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 fundamental limits of sampling strategies":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural network approximation of graph fourier transforms for sparse sampling of networked flow dynamics")

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.AI(Artificial Intelligence)