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 "sparse label propagation"):
  Abstract: We consider massive heterogeneous datasets with intrinsic network structure, i.e., big data over networks. These datasets can be modelled by graph signals, which are defined over large-scale irregular graphs representing complex networks. We show that (semi-supervised) learning of the entire underlying graph signal based on incomplete information provided by few initial labels can be reduced to a compressed sensing recovery problem within the cosparse analysis model. This reduction provides two things: First, it allows to apply highly developed compressed sensing methods to the learning problem. In particular, by implementing a recent primal-dual method for convex optimization, we obtain a sparse label propagation algorithm. Moreover, by casting the learning problem within compressed sensing, we are able to derive sufficient conditions on the graph structure and available label information, such that sparse label propagation is accurate.

Includes members:
  Member "semi supervised learning via sparse label propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "clustering in partially labeled stochastic block models via total variation minimization")
  Member "discrete signal processing on graphs sampling theory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "network lasso clustering and optimization in large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural generators of sparse local linear models for achieving both accuracy and interpretability")
  Member "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "analysis ell_1 recovery with frames and gaussian measurements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive sensing with redundant dictionaries and structured measurements")

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.LG(Machine Learning)