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 "signals on graphs uncertainty principle and sampling"):
  Abstract: In many applications, the observations can be represented as a signal defined over the vertices of a graph. The analysis of such signals requires the extension of standard signal processing tools. In this paper, first, we provide a class of graph signals that are maximally concentrated on the graph domain and on its dual. Then, building on this framework, we derive an uncertainty principle for graph signals and illustrate the conditions for the recovery of band-limited signals from a subset of samples. We show an interesting link between uncertainty principle and sampling and propose alternative signal recovery algorithms, including a generalization to frame-based reconstruction methods. After showing that the performance of signal recovery algorithms is significantly affected by the location of samples, we suggest and compare a few alternative sampling strategies. Finally, we provide the conditions for perfect recovery of a useful signal corrupted by sparse noise, showing that this problem is also intrinsically related to vertex-frequency localization properties.

Includes members:
  Member "sampling of graph signals with successive local aggregations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graphbgs background subtraction via recovery of graph signals")
  Member "toward an uncertainty principle for weighted graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive least mean squares estimation of graph signals")
  Member "uncertainty principle and sampling of signals defined on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph sampling for covariance estimation")
  Member "local set based graph signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "on the degrees of freedom of signals on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed adaptive learning of graph signals")
  Member "near optimal sensor placement for linear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "greedy sampling of graph signals")
  Member "discrete signal processing on graphs frequency analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "edgenets edge varying graph neural networks")

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.DM(Discrete Mathematics)