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 recovery on graphs fundamental limits of sampling strategies"):
  Abstract: This paper builds theoretical foundations for the recovery of a newly proposed class of smooth graph signals, approximately bandlimited graph signals, under three sampling strategies: uniform sampling, experimentally designed sampling and active sampling. We then state minimax lower bounds on the maximum risk for the approximately bandlimited class under three sampling strategies and show that active sampling cannot fundamentally outperform experimentally designed sampling. We then propose a recovery strategy to compare uniform sampling with experimentally designed sampling. As the proposed recovery strategy lends itself well to statistical analysis, we derive the exact mean square error of each sampling strategy. To study convergence rates, we introduce two types of graphs and find that (1) the proposed recovery strategies achieve the optimal rates; and (2) the experimentally designed sampling fundamentally outperforms uniform sampling for type-2 class of graphs. To validate our proposed recovery strategies, we test them on five specific graphs: a ring graph with k nearest neighbors, an ErdősRenyi graph graph, a random geometric graph, a small-world graph and a power-law graph and find that experimental results match the proposed theory well. This work shows a comprehensive explanation for when and why sampling for semi-supervised learning with graphs works.

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 "subgraph based filterbanks for graph signals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "region adaptive graph fourier transform for 3d point clouds")
  Member "compressive spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "sampling of graph signals with successive local aggregations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graphbgs background subtraction via recovery of graph signals")
  Member "signals on graphs uncertainty principle and sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "active learning for graph neural networks via node feature propagation")
  Member "signal representations on graphs tools and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards interpretable sparse graph representation learning with laplacian pooling")

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)