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 "decentralized joint sparse signal recovery a sparse bayesian learning approach"):
  Abstract: This work proposes a decentralized, iterative, sparse Bayesian learning algorithm for in-network estimation of multiple joint-sparse vectors by a network of nodes, using noisy and underdetermined linear measurements. The proposed algorithm, called consensus-based distributed sparse Bayesian learning, exploits the network wide joint sparsity of the unknown sparse vectors to recover them from significantly fewer number of local measurements compared to standalone sparse signal recovery schemes. To reduce the amount of internode communication and the associated overheads, the nodes exchange messages with only a small set of bridge nodes. Under this communication scheme, we separately analyze the convergence of the underlying bridge node-based alternating direction method of multiplier (ADMM) iterations used in our proposed algorithm and establish its linear convergence rate. The findings from the convergence analysis of decentralized ADMM are used to accelerate the convergence of the proposed algorithm. Using Monte Carlo simulations as well as real-world-data-based experiments, we demonstrate the superior performance of our proposed algorithm compared to existing decentralized algorithms: DRL-1, DCOMP, and DCSP.

Includes members:
  Member "decentralized subspace pursuit for joint sparsity pattern recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decentralized and collaborative subspace pursuit a communication efficient algorithm for joint sparsity pattern recovery with sensor networks")
  Member "efficient high dimensional inference in the multiple measurement vector problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "wideband spectrum sensing for cognitive radio networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "20 years of evolution from cognitive to intelligent communications")
  Member "cooperative sparsity pattern recovery in distributed networks via distributed omp":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fusion based cooperative support identification for compressive networked sensing")
  Member "estimation of sparse mimo channels with common support":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain")
  Member "a fast distributed proximal gradient method":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed big data optimization via block communications")
  Member "fast distributed gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on local computation for optimization in multi agent systems")
  Member "optimization with sparsity inducing penalties":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse optimization for green edge ai inference")

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)