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 "on the duality between network flows and network lasso"):
  Abstract: The data arising in many application domains have an intrinsic network structure. Such network structure is computationally apprealing due to the availability of highly scalable graph algorithms. An important class of graph algorithms is related to optimizing network flows. This paper explores the duality of network flow methods and the recently proposed network Lasso. Network Lasso extends the Lasso method from sparse linear models to clustered graph signals. It turns out that the computational and statistical properties of network Lasso crucially depends on the existence of sufficiently large network flows. Using elementary tools from convex analysis, we offer a precise characterization of the duality between network Lasso and a minimum cost network flow problem. This duality provides a strong link between network Lasso methods and network flow algorithms.

Includes members:
  Member "greedy sampling of graph signals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural network approximation of graph fourier transforms for sparse sampling of networked flow dynamics")
  Member "network lasso clustering and optimization in large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "approximating spectral clustering via sampling a review")

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)