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 "a gamp based low complexity sparse bayesian learning algorithm"):
  Abstract: In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning (SBL). In particular, GGAMP is used to implement the E-step in SBL in place of matrix inversion, leveraging the fact that GGAMP is guaranteed to converge with appropriate damping. The resulting GGAMP-SBL algorithm is much more robust to arbitrary measurement matrix $\boldsymbol{A}$ than the standard damped GAMP algorithm while being much lower complexity than the standard SBL algorithm. We then extend the approach from the single measurement vector (SMV) case to the temporally correlated multiple measurement vector (MMV) case, leading to the GGAMP-TSBL algorithm. We verify the robustness and computational advantages of the proposed algorithms through numerical experiments.

Includes members:
  Member "type i and type ii bayesian methods for sparse signal recovery using scale mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relevance subject machine a novel person re identification framework")
  Member "adaptive damping and mean removal for the generalized approximate message passing algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "inference for generalized linear models via alternating directions and bethe free energy minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an approximate message passing framework for side information")
  Member "fixed points of generalized approximate message passing with arbitrary matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "efficient high dimensional inference in the multiple measurement vector problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "expectation maximization gaussian mixture approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo")
  Member "the dynamics of message passing on dense graphs with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "generalized approximate message passing for estimation with random linear mixing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")

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)