human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "time varying downlink channel tracking for quantized massive mimo networks"):
  with members:
    - "beamspace aware adaptive channel estimation for single carrier time varying massive mimo channels"
    - "interference alignment and soft space reuse based cooperative transmission for multi cell massive mimo networks"
    - "bayesian optimal data detector for mmwave ofdm system with low resolution adc"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "training sequence design for feedback assisted hybrid beamforming in massive mimo systems"
    - "two tier precoding for fdd multi cell massive mimo time varying interference networks"

Connected through hyperedge HE-2 (induced by source paper "passive beamforming and information transfer design for reconfigurable intelligent surfaces aided multiuser mimo systems"):
  with members:
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "passive beamforming and information transfer via large intelligent surface"
    - "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "towards smart and reconfigurable environment intelligent reflecting surface aided wireless network"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"

Connected through hyperedge HE-3 (induced by source paper "sparse bayesian learning using approximate message passing with unitary transformation"):
  with members:
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "approximate message passing with unitary transformation"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "rectified gaussian scale mixtures and the sparse non negative least squares problem"):
  with members:
    - "type i and type ii bayesian methods for sparse signal recovery using scale mixtures"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "spatiotemporal sparse bayesian learning with applications to compressed sensing of multichannel physiological signals"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "robust face recognition via block sparse bayesian learning"
    - "expectation maximization gaussian mixture approximate message passing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

Question: Please tell me which class the center node 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)